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1
Matrosov, Sergey Y. "Attenuation-Based Estimates of Rainfall Rates Aloft with Vertically Pointing Ka-Band Radars." Journal of Atmospheric and Oceanic Technology 22, no. 1 (January 1, 2005): 43–54. http://dx.doi.org/10.1175/jtech-1677.1.
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Abstract An approach is suggested to retrieve low-resolution rainfall rate profiles and layer-averaged rainfall rates, Ra, from radar reflectivity measurements made by vertically pointing Ka-band radars. This approach is based on the effects of attenuation of radar signals in rain and takes advantage of the nearly linear relation between specific attenuation and rainfall rate at Ka-band frequencies. The variability of this relation due to temperature, details of raindrop size distributions, and the nature of rain (convective versus stratiform) is rather small (∼10%) and contributes little to errors in rainfall rate retrievals. The main contribution to the retrieval errors comes from the uncertainty of the difference in the nonattenuated radar reflectivities in the beginning and the end of the range resolution interval. For 2- and 1-dB uncertainties in this difference, the retrieval errors due to this main contribution are less than 34% and 17%, correspondingly, for rains with Ra ≈ 10 mm h−1 at a 1-km resolution interval. The heavier rain rates are retrieved with a better accuracy since this retrieval error contribution is proportional to 1/Ra. The retrieval accuracy can also be improved but at the expense of more coarse vertical resolutions of retrievals since the main retrieval error contribution is also proportional to the reciprocal of the resolution interval. The Mie scattering effects at Ka band results in less variability in nonattenuated reflectivities (cf. lower radar frequencies), which aids the suggested approach. Given that radar receivers are not saturated, the rainfall rates can be retrieved using cloud radars that were originally designed for measuring only nonprecipitating and weakly precipitating clouds. An important advantage of the attenuation-based retrievals of rainfall is that absolute radar calibration is not required. The inclusion of rainfall information will improve the characterization of the atmospheric column obtained with such radars used for climate research. The applications of the suggested approach are illustrated using the vertically pointing Ka-band radar measurements made during a field experiment in southern Florida. The retrieval results are in good agreement with surface estimates of rainfall rates.
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Hristova-Veleva, S. M., P. S. Callahan, R. S. Dunbar, B. W. Stiles, S. H. Yueh, J. N. Huddleston, S. V. Hsiao, et al. "Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description." Journal of Applied Meteorology and Climatology 52, no. 12 (December 2013): 2828–48. http://dx.doi.org/10.1175/jamc-d-12-0237.1.
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AbstractScatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a “rain indicator” to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.
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Hilburn, K. A., and F. J. Wentz. "Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)." Journal of Applied Meteorology and Climatology 47, no. 3 (March 1, 2008): 778–94. http://dx.doi.org/10.1175/2007jamc1635.1.
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Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.
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Linkova, A. "Сonsideration of the signal attenuation in double-frequency sensing for rain intensity retrieval." RADIOFIZIKA I ELEKTRONIKA 26, no. 3 (2021): 3–10. http://dx.doi.org/10.15407/rej2021.03.003.
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Subject and Purpose. Precipitation is the main source of agricultural land moisture. The knowledge of its amount, especially during the growing season, is important information to justify necessary agronomic and land reclamation measures. The purpose of this work is to solve by regularization the inverse problem of double-frequency sensing of precipitation in the microwave range with the signal attenuation considered and analyze the influence of radar cross-section (RCS) calculation errors and the total signal attenuation measuring precision on the rain intensity retrieval results. Methods and Methodology. Numerical simulation is used in double frequency retrievals to solve the integral scattering equation by regularization methods. Results. Numerical simulation has been performed for the rain intensity retrieval with a uniform spatial profile of rain intensity in the range 1…20 mm/h. Direct and inverse iterative procedures were used for having the signal attenuation at 0.82 and 3.2 cm operating wavelengths. It has been shown that the direct iterative procedure is less effective than the inverse one. Thus, when the rain intensity exceeds 20 mm/h or when it is within 10…20 mm/h and a rain spatial extent goes over 500 m, the direct iteration scheme causes significant errors in the rain intensity retrieval. Conclusion. The analysis of the results has shown that the use of the inverse iterative procedure makes it possible to retrieve a uniform-profile rain intensity with a 25 % error for rains with a 20 mm/h intensity and a 4 km spatial extent and ± 20 % errors in the total signal attenuation and specific RCS calculated.
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Duncan, David Ian, Christian D. Kummerow, Brenda Dolan, and Veljko Petković. "Towards variational retrieval of warm rain from passive microwave observations." Atmospheric Measurement Techniques 11, no. 7 (July 25, 2018): 4389–411. http://dx.doi.org/10.5194/amt-11-4389-2018.
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Abstract. An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rainwater during iteration. The state-dependent errors and nuanced treatment of drop distributions in precipitating regions are novel and may be applicable for future retrievals and all-sky data assimilation methods. This retrieval method can effectively increase passive microwave sensors' sensitivity to light rainfall that might otherwise be missed. Comparisons with space-borne and ground radar estimates are provided as a proof of concept, demonstrating that a passive-only variational retrieval can be sufficiently constrained from non-raining through warm rain conditions. Significant deviations from forward model assumptions cause non-convergence, usually a result of scattering hydrometeors above the freezing level. However, for cases with liquid-only precipitation, this retrieval displays greater sensitivity than a benchmark operational retrieval. Analysis against passive and active products from the Global Precipitation Measurement (GPM) satellite shows substantial discrepancies in precipitation frequency, with the experimental retrieval observing more frequent light rain. This approach may be complementary to other precipitation retrievals, and its potential synergy with the operational passive GPM retrieval is briefly explored. There are also implications for data assimilation, as all 13 channels on the GPM Microwave Imager (GMI) are simulated over ocean with fidelity in warm raining conditions.
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Kwon, Eun-Han, Byung-Ju Sohn, Dong-Eon Chang, Myoung-Hwan Ahn, and Song Yang. "Use of Numerical Forecasts for Improving TMI Rain Retrievals over the Mountainous Area in Korea." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1995–2007. http://dx.doi.org/10.1175/2007jamc1857.1.
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Abstract Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm.
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Wang, Wenyue, Klemens Hocke, and Christian Mätzler. "Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry." Remote Sensing 13, no. 11 (June 5, 2021): 2217. http://dx.doi.org/10.3390/rs13112217.
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Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.
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Wu, P., X. Dong, and B. Xi. "Marine boundary layer drizzle properties and their impact on cloud property retrieval." Atmospheric Measurement Techniques 8, no. 9 (September 3, 2015): 3555–62. http://dx.doi.org/10.5194/amt-8-3555-2015.
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Abstract. In this study, we retrieve and document drizzle properties, and investigate the impact of drizzle on cloud property retrieval in Dong et al. (2014a) from ground-based measurements at the ARM Azores facility from June 2009 to December 2010. For the selected cloud and drizzle samples, the drizzle occurrence is 42.6 %, with a maximum of 55.8 % in winter and a minimum of 35.6 % in summer. The annual means of drizzle liquid water path LWPd, effective radius rd, and number concentration Nd for the rain (virga) samples are 4.73 (1.25) g m−2, 61.5 (36.4) μm, and 0.38 (0.79) cm−3. The seasonal mean LWPd values are less than 3 % of the LWP values retrieved by the microwave radiometer (MWR). The annual mean differences in cloud-droplet effective radius with and without drizzle are 0.75 and 2.35 %, respectively, for the virga and rain samples. Therefore, we conclude that the impact of drizzle below the cloud base on cloud property retrieval is insignificant for a solar-transmission-based method, but significant for any retrievals using radar reflectivity.
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Brown, Paula J., Christian D. Kummerow, and David L. Randel. "Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones." Journal of Atmospheric and Oceanic Technology 33, no. 7 (July 2016): 1539–56. http://dx.doi.org/10.1175/jtech-d-15-0234.1.
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AbstractThe Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
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Carlin, Jacob T., Alexander V. Ryzhkov, Jeffrey C. Snyder, and Alexander Khain. "Hydrometeor Mixing Ratio Retrievals for Storm-Scale Radar Data Assimilation: Utility of Current Relations and Potential Benefits of Polarimetry." Monthly Weather Review 144, no. 8 (August 1, 2016): 2981–3001. http://dx.doi.org/10.1175/mwr-d-15-0423.1.
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Abstract The assimilation of radar data into storm-scale numerical weather prediction models has been shown to be beneficial for successfully modeling convective storms. Because of the difficulty of directly assimilating reflectivity (Z), hydrometeor mixing ratios, and sometimes rainfall rate, are often retrieved from Z observations using retrieval relations, and are assimilated as state variables. The most limiting (although widely employed) cases of these relations are derived, and their assumptions and limitations are discussed. To investigate the utility of these retrieval relations for liquid water content (LWC) and ice water content (IWC) in rain and hail as well as the potential for improvement using polarimetric variables, two models with spectral bin microphysics coupled with a polarimetric radar operator are used: a one-dimensional melting hail model and the two-dimensional Hebrew University Cloud Model. The relationship between LWC and Z in pure rain varies spatially and temporally, with biases clearly seen using the normalized number concentration. Retrievals using Z perform the poorest while specific attenuation and specific differential phase shift (KDP) perform much better. Within rain–hail mixtures, separate estimation of LWC and IWC is necessary. Prohibitively large errors in the retrieved LWC may result when using Z. The quantity KDP can be used to effectively retrieve the LWC and to isolate the contribution of IWC to Z. It is found that the relationship between Z and IWC is a function of radar wavelength, maximum hail diameter, and principally the height below the melting layer, which must be accounted for in order to achieve accurate retrievals.
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Viltard, Nicolas, Corinne Burlaud, and Christian D. Kummerow. "Rain Retrieval from TMI Brightness Temperature Measurements Using a TRMM PR–Based Database." Journal of Applied Meteorology and Climatology 45, no. 3 (March 1, 2006): 455–66. http://dx.doi.org/10.1175/jam2346.1.
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Abstract This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and −30%, respectively) and a minimum error between 1 and 7 mm h−1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation.
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Fisher, Brad, and David B. Wolff. "Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR." Journal of Applied Meteorology and Climatology 50, no. 5 (May 2011): 994–1023. http://dx.doi.org/10.1175/2010jamc2487.1.
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AbstractPassive and active microwave rain sensors on board Earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscures the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different spaceborne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands, and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean, and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite, and the regional climatology. The most significant result from this study found that each of the satellites incurred negative long-term oceanic retrieval biases of 10%–30%.
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Birman, Camille, Fatima Karbou, and Jean-François Mahfouf. "Daily Rainfall Detection and Estimation over Land Using Microwave Surface Emissivities." Journal of Applied Meteorology and Climatology 54, no. 4 (April 2015): 880–95. http://dx.doi.org/10.1175/jamc-d-14-0192.1.
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AbstractSurface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.
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SETO, Shinta. "RETRIEVAL ERROR AND RAIN/NO-RAIN CLASSIFICATION ERROR." PROCEEDINGS OF HYDRAULIC ENGINEERING 49 (2005): 271–76. http://dx.doi.org/10.2208/prohe.49.271.
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Zhang, Ruanyu, Zhenzhan Wang, and Kyle Hilburn. "Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm." Remote Sensing 10, no. 11 (November 8, 2018): 1770. http://dx.doi.org/10.3390/rs10111770.
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A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10–15 mm/h category; however, with maximum errors around −22% at the lowest (0–0.5 mm/h) and highest rain rates (25–30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals.
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Wolff, David B., Walter A. Petersen, Ali Tokay, David A. Marks, and Jason L. Pippitt. "Assessing Dual-Polarization Radar Estimates of Extreme Rainfall during Hurricane Harvey." Journal of Atmospheric and Oceanic Technology 36, no. 12 (December 1, 2019): 2501–20. http://dx.doi.org/10.1175/jtech-d-19-0081.1.
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Abstract Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.
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Xie, Xuetong, Jing Wang, and Mingsen Lin. "A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements." Remote Sensing 12, no. 10 (May 21, 2020): 1648. http://dx.doi.org/10.3390/rs12101648.
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The backscattering coefficients measured by Ku-band scatterometers are strongly affected by rainfall, resulting in a systematic error in sea surface wind field retrieval. In rainy conditions, the radar signals are subject to absorption by the raindrops in their round-trip propagation through the atmosphere, while the backscatter of raindrops raises the echo energy. In addition, raindrops give rise to roughness by impinging the ocean surface, resulting in an increase in the echo energy measured by a scatterometer. Under moderate wind conditions, the comprehensive impact of rainfall causes the wind speeds retrieved by the scatterometer to be higher than their actual values. The HY-2A scatterometer is a Ku-band, pencil-beam, conically scanning scatterometer. To correct the systematic error of the HY-2A scatterometer measurement in rainy conditions, a neural network model is proposed according to the characteristics of the backscatter coefficients measured by the HY-2A scatterometer in the presence of rain. With the neural network, the wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data were used as the reference to correct the deviation in backscatter coefficients measured by the HY-2A scatterometer in rainy conditions, and the accuracy in wind speeds retrieved using the corrected backscatter coefficients was significantly improved. Compared with the cases of wind retrieval without rain effect correction, the wind speeds retrieved from the corrected backscatter coefficients by the neural network show a much lower systematic deviation, which indicates that the neural network can effectively remove the systematic deviation in the backscatter coefficients and the retrieved wind speeds caused by rain.
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Tadesse, Alemu, and Emmanouil N. Anagnostou. "The Effect of Storm Life Cycle on Satellite Rainfall Estimation Error." Journal of Atmospheric and Oceanic Technology 26, no. 4 (April 1, 2009): 769–77. http://dx.doi.org/10.1175/2008jtecha1129.1.
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Abstract The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA’s Multisatellite Precipitation Analysis–Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km–hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earth-orbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge–calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy.
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Laviola, Sante, Agata Moscatello, Mario Marcello Miglietta, Elsa Cattani, and Vincenzo Levizzani. "Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean." Journal of Hydrometeorology 12, no. 4 (August 1, 2011): 634–49. http://dx.doi.org/10.1175/2011jhm1257.1.
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Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.
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Fisher, Brad L. "Statistical Error Decomposition of Regional-Scale Climatological Precipitation Estimates from the Tropical Rainfall Measuring Mission (TRMM)." Journal of Applied Meteorology and Climatology 46, no. 6 (June 1, 2007): 791–813. http://dx.doi.org/10.1175/jam2497.1.
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Abstract Monthly rainfall estimates inferred from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite contain errors due to discrete temporal sampling and remote spaceborne rain retrievals. This paper develops a regional-scale error model that uses the rain information in the ground data to disentangle the sampling and retrieval errors in the satellite estimate statistically. The proposed method computes a mean rain rate from monthly rainfall statistics for each TRMM rain sensor by subsampling high-resolution ground-based rain data at satellite overpass times. This additional rain-subsampled parameter plays an essential role in the statistical decomposition of the total error distribution into its sampling and retrieval error components. Using the statistical formalism developed in this study, an error analysis was performed on 5 yr of monthly rain estimates produced by the TRMM Microwave Imager (TMI) and precipitation radar (PR) rain sensors aboard TRMM over a quasi 2° × 2° region of the TRMM ground validation (GV) site at Melbourne, Florida. Annual retrieval and sampling error statistics were computed for the TMI and PR using monthly rainfall estimates derived from two independent ground-based sensors: a regional rain gauge network and the Next-Generation Weather Radar (NEXRAD). Subsampled ground-based rainfall estimates produced for the radar and the gauges were highly correlated with the TMI and PR rainfall estimates, and both GV sensors produced relatively consistent error estimates. The PR-to-TMI sampling error ratio was equal to about 1.3, which was in close agreement with prelaunch predications, and the TMI-to-PR retrieval error ratio was about 2.0. For the TMI, a seasonally alternating rainfall bias was also observed that was negative during winter and positive during summer.
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Katsanos, D., N. Viltard, K. Lagouvardos, and V. Kotroni. "Performance of a rain retrieval algorithm using TRMM data in the Eastern Mediterranean." Advances in Geosciences 7 (May 2, 2006): 321–25. http://dx.doi.org/10.5194/adgeo-7-321-2006.
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Abstract. This study aims to make a regional characterization of the performance of the rain retrieval algorithm BRAIN. This algorithm estimates the rain rate from brightness temperatures measured by the TRMM Microwave Imager (TMI) onboard the TRMM satellite. In this stage of the study, a comparison between the rain estimated from Precipitation Radar (PR) onboard TRMM (2A25 version 5) and the rain retrieved by the BRAIN algorithm is presented, for about 30 satellite overpasses over the Central and Eastern Mediterranean during the period October 2003–March 2004, in order to assess the behavior of the algorithm in the Eastern Mediterranean region. BRAIN was built and tested using PR rain estimates distributed randomly over the whole TRMM sampling region. Characterization of the differences between PR and BRAIN over a specific region is thus interesting because it might show some local trend for one or the other of the instrument. The checking of BRAIN results against the PR rain-estimate appears to be consistent with former results i.e. a somewhat marked discrepancy for the highest rain rates. This difference arises from a known problem that affect rain retrieval based on passive microwave radiometers measurements, but some of the higher radar rain rates could also be questioned. As an independent test, a good correlation between the rain retrieved by BRAIN and lighting data (obtained by the UK Met. Office long range detection system) is also emphasized in the paper.
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Qiu, Shuang, Paul Pellegrino, Ralph Ferraro, and Limin Zhao. "The Improved AMSU Rain-Rate Algorithm and Its Evaluation for a Cool Season Event in the Western United States." Weather and Forecasting 20, no. 5 (October 1, 2005): 761–74. http://dx.doi.org/10.1175/waf880.1.
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Abstract Rain-rate retrievals from passive microwave sensors are useful for a number of applications related to weather forecasting. For example, in the United States, such estimates are useful for offshore rainfall systems approaching land and in regions where the Weather Surveillance Radar-1988 Doppler (WSR-88D) network is inadequate. Improvements have been made to the rain-rate retrieval from the Advanced Microwave Sounding Unit (AMSU) on board the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POESs). The new features of the improved rain-rate algorithm include a two-stream correction of the satellite brightness temperatures at 89 and 150 GHz, cloud- and rain-type classification for better retrieval of rain rate, and removal of the two ad hoc thresholds in the ice water path (IWP) and effective diameter (De) retrieval where the scattering signals are very small. In this paper, the new algorithm has been compared to the previous NOAA operational algorithm. In particular, the better utilization of the measurements at and above 150 GHz is shown to produce improved sensitivity to light rainfall associated with winter season storm systems. This improvement is demonstrated through a wintertime case study over southern California during February 2003.
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Matrosov, Sergey Y., Alessandro Battaglia, and Peter Rodriguez. "Effects of Multiple Scattering on Attenuation-Based Retrievals of Stratiform Rainfall from CloudSat." Journal of Atmospheric and Oceanic Technology 25, no. 12 (December 1, 2008): 2199–208. http://dx.doi.org/10.1175/2008jtecha1095.1.
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Abstract An attenuation-based method to retrieve vertical profiles of rainfall rates from height derivatives/gradients of CloudSat nadir-pointing W-band reflectivity measurements is discussed. This method takes advantage of the high attenuation of W-band frequency signals in rain and the low variability of nonattenuated reflectivity due to strong non-Rayleigh scattering from rain drops. The retrieval uncertainties could reach 40%–50%. The suggested method is generally applicable to rainfall rates (R) in an approximate range from about 2–3 to about 20–25 mm h−1. Multiple scattering noticeably affects the gradients of CloudSat measurements for R values greater than about 5 mm h−1. To avoid a retrieval bias caused by multiple-scattering effects, a special correction for retrievals is introduced. For rainfall rates greater than about 25 mm h−1, the influence of multiple scattering gets overwhelming, and the retrievals become problematic, especially for rainfalls with higher freezing-level altitudes. The attenuation-based retrieval method was applied to experimental data from CloudSat covering the range of rainfall rates. CloudSat retrievals were compared to the rainfall estimates available from a National Weather Service ground-based scanning precipitation radar operating at S band. Comparisons between spaceborne and conventional radar rainfall retrievals were generally in good agreement and indicated the mutual consistency of both quantitative precipitation estimate types. The suggested CloudSat rainfall retrieval method is immune to the absolute calibration of the radar and to attenuation caused by the melting layer and snow regions. Since it does not require surface returns, it is applicable to measurements above both land and water surfaces.
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Mróz, Kamil, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori. "Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study." Atmospheric Measurement Techniques 14, no. 1 (January 25, 2021): 511–29. http://dx.doi.org/10.5194/amt-14-511-2021.
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Abstract. This study investigates the link between rain and ice microphysics across the melting layer in stratiform rain systems using measurements from vertically pointing multi-frequency Doppler radars. A novel methodology to examine the variability of the precipitation rate and the mass-weighted melted diameter (Dm) across the melting region is proposed and applied to a 6 h long case study, observed during the TRIPEx-pol field campaign at the Jülich Observatory for Cloud Evolution Core Facility and covering a gamut of ice microphysical processes. The methodology is based on an optimal estimation (OE) retrieval of particle size distributions (PSDs) and dynamics (turbulence and vertical motions) from observed multi-frequency radar Doppler spectra applied both above and below the melting layer. First, the retrieval is applied in the rain region; based on a one-to-one conversion of raindrops into snowflakes, the retrieved drop size distributions (DSDs) are propagated upward to provide the mass-flux-preserving PSDs of snow. These ice PSDs are used to simulate radar reflectivities above the melting layer for different snow models and they are evaluated for a consistency with the actual radar measurements. Second, the OE snow retrieval where Doppler spectra are simulated based on different snow models, which consistently compute fall speeds and electromagnetic properties, is performed. The results corresponding to the best-matching models are then used to estimate snow fluxes and Dm, which are directly compared to the corresponding rain quantities. For the case study, the total accumulation of rain (2.30 mm) and the melted equivalent accumulation of snow (1.93 mm) show a 19 % difference. The analysis suggests that the mass flux through the melting zone is well preserved except the periods of intense riming where the precipitation rates were higher in rain than in the ice above. This is potentially due to additional condensation within the melting zone in correspondence to high relative humidity and collision and coalescence with the cloud droplets whose occurrence is ubiquitous with riming. It is shown that the mean mass-weighted diameter of ice is strongly related to the characteristic size of the underlying rain except the period of extreme aggregation where breakup of melting snowflakes significantly reduces Dm. The proposed methodology can be applied to long-term observations to advance our knowledge of the processes occurring across the melting region; this can then be used to improve assumptions underpinning spaceborne radar precipitation retrievals.
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Klotz, Bradley W., and Eric W. Uhlhorn. "Improved Stepped Frequency Microwave Radiometer Tropical Cyclone Surface Winds in Heavy Precipitation." Journal of Atmospheric and Oceanic Technology 31, no. 11 (November 2014): 2392–408. http://dx.doi.org/10.1175/jtech-d-14-00028.1.
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AbstractSurface wind speeds retrieved from airborne stepped frequency microwave radiometer (SFMR) brightness temperature measurements are important for estimating hurricane intensity. The SFMR performance is highly reliable at hurricane-force wind speeds, but accuracy is found to degrade at weaker wind speeds, particularly in heavy precipitation. Specifically, a significant overestimation of surface wind speeds is found in these conditions, suggesting inaccurate accounting for the impact of rain on the measured microwave brightness temperature. In this study, the wind speed bias is quantified over a broad range of operationally computed wind speeds and rain rates, based on a large sample of collocated SFMR wind retrievals and global positioning system dropwindsonde surface-adjusted wind speeds. The retrieval bias is addressed by developing a new SFMR C-band relationship between microwave absorption and rain rate (κ−R) from National Oceanic and Atmospheric Administration WP-3D aircraft tail Doppler radar reflectivity and in situ Droplet Measurement Technologies Precipitation Imaging Probe measurements to more accurately model precipitation impacts. Absorption is found to be a factor of 2 weaker than is estimated by the currently operational algorithm. With this new κ–R relationship, surface wind retrieval bias is significantly reduced in the presence of rain at wind speeds weaker than hurricane force. At wind speeds greater than hurricane force where little bias exists, no significant change is found. Furthermore, maximum rain rates computed using the revised algorithm are around 50% greater than operational measurements, which is more consistent with maximum reflectivity-estimated rain rates in hurricanes.
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Zhuge, Xiao-Yong, Fan Yu, and Cheng-Wei Zhang. "Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate." Journal of Hydrometeorology 12, no. 6 (December 1, 2011): 1255–70. http://dx.doi.org/10.1175/2011jhm1373.1.
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Abstract This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.
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Matrosov, Sergey Y. "Intercomparisons of CloudSat and Ground-Based Radar Retrievals of Rain Rate over Land." Journal of Applied Meteorology and Climatology 53, no. 10 (October 2014): 2360–70. http://dx.doi.org/10.1175/jamc-d-14-0055.1.
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AbstractExperimental retrievals of rain rates using the CloudSat spaceborne 94-GHz radar reflectivity gradient method over land were evaluated by comparing them with standard estimates from ground-based operational S-band radar measurements, which are widely used for quantitative precipitation estimations. The comparisons were performed for predominantly stratiform precipitation events that occurred in the vicinity of the Weather Surveillance Radar-1988 Doppler (WSR-88D) KGWX and KSHV radars during the CloudSat overpasses in the vicinity of these ground radar sites. The standard reflectivity-based WSR-88D rain-rate retrievals used in operational practice were utilized as a reference for the CloudSat retrieval evaluation. Spaceborne and ground-based radar rain-rate estimates that were closely collocated in space and time were generally well correlated. The correlation coefficients were approximately 0.65 on average, and the mean relative biases were usually within ±35% for the whole dataset and for individual events with typical rain rates exceeding ~2 mm h−1. For events with lighter rainfall, higher biases and lower correlations were often present. The normalized mean absolute differences between satellite- and ground-based radar retrievals were on average ~60%, with an increasing trend for lighter rainfall. Such mean differences are comparable to combined retrieval errors from both ground-based and satellite radar remote sensing approaches. Evaluation of potential effects of partial beam blockage on the ground-based radar measurements was performed, and the influence of the choice of relation between WSR-88D reflectivity and rain rate that was utilized in the ground-based rain-rate retrievals was assessed.
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Linkova, A. "The influence of a fallacy in specific effective scattering surface evaluation on the result of double-frequency retrieval of rain intensity." RADIOFIZIKA I ELEKTRONIKA 26, no. 2 (2021): 16–22. http://dx.doi.org/10.15407/rej2021.02.016.
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Subject and Purpose. The amount of precipitation is important information for the agro-climatic justification of agro-technical and reclamation actions directly controlling crop yields. The inverse problem of rain intensity retrieval from the remote sensing data is an incorrect mathematical physical problem described by a nonlinear integral equation. The purpose of this work is to analyze how errors in the specific effective scattering surface evaluation affect the results of double-frequency retrieval of the rain intensity obtained through the inverse problem solution by the previously proposed method. Methods and Methodology. Numerical simulation by using an approach based on regularization techniques and intended for the integral scattering equation solution is carried out for double-frequency sensing in the microwave range Results. Numerical simulations of the rain intensity retrieval have been performed at the operating wavelengths 0.82 and 3.2 cm in the range 1…30 mm/h and for different values of received power errors. It has been shown that an error in the specific effective scattering surface evaluation has a greater effect on the reliability of the intensity retrieval in the shorter wavelength case. And it exerts practically no effect (not exceeding 5 %) at the longer wavelength and at the rain intensity below 15 mm/h, which, however, is true for heavier rains, too. Conclusion. The analysis of the results has shown that the error of the rain intensity retrieval remains within acceptable limits (below 20 %) provided that the error in the specific effective scattering surface evaluation does not exceed 15 % at the shorter wavelength. At the longer wavelength, it can reach 30 %.
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Cao, Qing, Guifu Zhang, Edward A. Brandes, and Terry J. Schuur. "Polarimetric Radar Rain Estimation through Retrieval of Drop Size Distribution Using a Bayesian Approach." Journal of Applied Meteorology and Climatology 49, no. 5 (May 1, 2010): 973–90. http://dx.doi.org/10.1175/2009jamc2227.1.
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Abstract This study proposes a Bayesian approach to retrieve raindrop size distributions (DSDs) and to estimate rainfall rates from radar reflectivity in horizontal polarization ZH and differential reflectivity ZDR. With this approach, the authors apply a constrained-gamma model with an updated constraining relation to retrieve DSD parameters. Long-term DSD measurements made in central Oklahoma by the two-dimensional video disdrometer (2DVD) are first used to construct a prior probability density function (PDF) of DSD parameters, which are estimated using truncated gamma fits to the second, fourth, and sixth moments of the distributions. The forward models of ZH and ZDR are then developed based on a T-matrix calculation of raindrop backscattering amplitude with the assumption of drop shape. The conditional PDF of ZH and ZDR is assumed to be a bivariate normal function with appropriate standard deviations. The Bayesian algorithm has a good performance according to the evaluation with simulated ZH and ZDR. The algorithm is also tested on S-band radar data for a mesoscale convective system that passed over central Oklahoma on 13 May 2005. Retrievals of rainfall rates and 1-h rain accumulations are compared with in situ measurements from one 2DVD and six Oklahoma Mesonet rain gauges, located at distances of 28–54 km from Norman, Oklahoma. Results show that the rain estimates from the retrieval agree well with the in situ measurements, demonstrating the validity of the Bayesian retrieval algorithm.
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Matrosov, S. Y. "Synergetic use of millimeter and centimeter wavelength radars for retrievals of cloud and rainfall parameters." Atmospheric Chemistry and Physics Discussions 10, no. 1 (January 18, 2010): 947–75. http://dx.doi.org/10.5194/acpd-10-947-2010.
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Abstract. A remote sensing approach for simultaneous retrievals of cloud and rainfall parameters in the vertical column above the US Department of Energy's (DOE) Climate Research Facility at the Tropical Western Pacific (TWP) Darwin site in Australia is described. This approach uses vertically pointing measurements from a DOE Ka-band radar and scanning measurements from a nearby C-band radar pointing toward the TWP Darwin site. Rainfall retrieval constraints are provided by data from a surface impact disdrometer. The approach is applicable to stratiform precipitating cloud systems when a separation between the liquid hydrometeor layer, which contains rainfall and liquid water clouds, and the ice hydrometeor layer is provided by the radar bright band. Absolute C-band reflectivities and Ka-band vertical reflectivity gradients in the liquid layer are used for retrievals of the mean layer rain rate and cloud liquid water path (CLWP). C-band radar reflectivities are also used to estimate ice water path (IWP) in regions above the melting layer. The retrieval uncertainties of CLWP and IWP for typical stratiform precipitation systems are about 500–800 g m−2 (for CLWP) and a factor of 2 (for IWP). The CLWP retrieval uncertainties increase with rain rate, so retrievals for higher rain rates may be impractical. The expected uncertainties of layer mean rain rate retrievals are around 20%, which, in part, is due to constraints available from the disdrometer data. The applicability of the suggested approach is illustrated for two characteristic events observed at the TWP Darwin site during the wet season of 2007. A future deployment of W-band radars at the DOE tropical Climate Research Facilities can improve CLWP estimate accuracies and provide retrievals for a wider range of stratiform precipitating cloud events.
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Matrosov, S. Y. "Synergetic use of millimeter- and centimeter-wavelength radars for retrievals of cloud and rainfall parameters." Atmospheric Chemistry and Physics 10, no. 7 (April 7, 2010): 3321–31. http://dx.doi.org/10.5194/acp-10-3321-2010.
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Abstract. A remote sensing approach for simultaneous retrievals of cloud and rainfall parameters in the vertical column above the US Department of Energy's (DOE) Climate Research Facility at the Tropical Western Pacific (TWP) Darwin site in Australia is described. This approach uses vertically pointing measurements from a DOE Ka-band radar and scanning measurements from a nearby C-band radar pointing toward the TWP Darwin site. Rainfall retrieval constraints are provided by data from a surface impact disdrometer. The approach is applicable to stratiform precipitating cloud systems when a separation between the liquid hydrometeor layer, which contains rainfall and liquid water clouds, and the ice hydrometeor layer is provided by the radar bright band. Absolute C-band reflectivities and Ka-band vertical reflectivity gradients in the liquid layer are used for retrievals of the mean layer rain rate and cloud liquid water path (CLWP). C-band radar reflectivities are also used to estimate ice water path (IWP) in regions above the melting layer. The retrieval uncertainties of CLWP and IWP for typical stratiform precipitation systems are about 500–800 g m−2 (for CLWP) and a factor of 2 (for IWP). The CLWP retrieval uncertainties increase with rain rate, so retrievals for higher rain rates may be impractical. The expected uncertainties of layer mean rain rate retrievals are around 20%, which, in part, is due to constraints available from the disdrometer data. The applicability of the suggested approach is illustrated for two characteristic events observed at the TWP Darwin site during the wet season of 2007. A future deployment of W-band radars at the DOE tropical Climate Research Facilities can improve CLWP estimation accuracies and provide retrievals for a wider range of stratiform precipitating cloud events.
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Fiorino, Steven T., and Eric A. Smith. "Critical Assessment of Microphysical Assumptions within TRMM Radiometer Rain Profile Algorithm Using Satellite, Aircraft, and Surface Datasets from KWAJEX." Journal of Applied Meteorology and Climatology 45, no. 5 (May 1, 2006): 754–86. http://dx.doi.org/10.1175/jam2336.1.
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Abstract The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager precipitation profile retrieval algorithm (2a12) assumes cloud model–derived vertically distributed microphysics as part of the radiative transfer–controlled inversion process to generate rain-rate estimates. Although this algorithm has been extensively evaluated, none of the evaluation approaches has explicitly examined the underlying microphysical assumptions through a direct intercomparison of the assumed cloud-model microphysics with in situ, three-dimensional microphysical observations. The main scientific objective of this study is to identify and overcome the foremost model-generated microphysical weaknesses in the TRMM 2a12 algorithm through analysis of (a) in situ aircraft microphysical observations; (b) aircraft- and satellite-based passive microwave measurements; (c) ground-, aircraft-, and satellite-based radar measurements; (d) synthesized satellite brightness temperatures and radar reflectivities; (e) radiometer-only profile algorithm retrievals; and (f) radar-only profile or volume algorithm retrievals. Results indicate the assumed 2a12 microphysics differs most from aircraft-observed microphysics where either ground or aircraft radar–derived rain rates exhibit the greatest differences with 2a12-retrieved rain rates. An emission–scattering coordinate system highlights the 2a12 algorithm's tendency to match high-emission/high-scattering observed profiles to high-emission/low-scattering database profiles. This is due to a lack of mixed-phase-layer ice hydrometeor scatterers in the cloud model–generated profiles as compared with observed profiles. Direct comparisons between aircraft-measured and model-generated 2a12 microphysics suggest that, on average, the radiometer algorithm's microphysics database retrieves liquid and ice water contents that are approximately 1/3 the size of those observed at levels below 10 km. Also, the 2a12 rain-rate retrievals are shown to be strongly influenced by the 2a12's convective fraction specification. A proposed modification of this factor would improve 2a12 rain-rate retrievals; however, fundamental changes to the cloud radiation model's ice parameterization are necessary to overcome the algorithm's tendency to produce mixed-phase-layer ice hydrometeor deficits.
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Rapp, Anita D., G. Elsaesser, and C. Kummerow. "A Combined Multisensor Optimal Estimation Retrieval Algorithm for Oceanic Warm Rain Clouds." Journal of Applied Meteorology and Climatology 48, no. 11 (November 1, 2009): 2242–56. http://dx.doi.org/10.1175/2009jamc2156.1.
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Abstract The complicated interactions between cloud processes in the tropical hydrologic cycle and their responses to changes in environmental variables have been the focus of many recent investigations. Most studies that examine the response of the hydrologic cycle to temperature changes focus on deep convection and cirrus production, but recent results suggest that warm rain clouds may be more sensitive to temperature changes. These clouds are prevalent in the tropics and make considerable contributions to the radiation budget and to total tropical rainfall, as well as serving to moisten and precondition the atmosphere for deep convection. A change in the properties of these clouds in climate-change scenarios could have significant implications for the hydrologic cycle. Existing microwave and visible retrievals of warm rain cloud liquid water path (LWP) disagree over the range of sea surface temperatures (SST) observed in the tropical western Pacific Ocean. Although both retrieval methods show similar behavior for nonraining clouds, the two methods show very different warm-rain-cloud LWP responses to SST, both in magnitude and trend. This makes changes to the relationship between precipitation and cloud properties in changing temperature regimes difficult to interpret. A combined optimal estimation retrieval algorithm that takes advantage of the strengths of the different satellite measurements available on the Tropical Rainfall Measuring Mission (TRMM) satellite has been developed. Deconvolved TRMM Microwave Imager brightness temperatures are combined with cloud fraction from the Visible and Infrared Scanner and rainwater estimates from the TRMM precipitation radar to retrieve the cloud LWP in warm rain systems. This algorithm is novel in that it takes into account the water in the rain and estimates the LWP due to only the cloud water in a raining cloud, thus allowing investigation of the effects of precipitation on cloud properties.
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Ryu, Geun-Hyeok, Byung-Ju Sohn, Christian D. Kummerow, Eun-Kyoung Seo, and Gregory J. Tripoli. "Rain-Rate Characteristics over the Korean Peninsula and Improvement of the Goddard Profiling (GPROF) Database for TMI Rainfall Retrievals." Journal of Applied Meteorology and Climatology 51, no. 4 (April 2012): 786–98. http://dx.doi.org/10.1175/jamc-d-11-094.1.
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AbstractSummer rainfall characteristics over the Korean Peninsula are examined using six years of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements and surface rain measurements from the densely populated rain gauges spread across South Korea. A comparison of the TMI brightness temperature at 85 GHz with the measured surface rain rate reveals that a significant portion of rainfall over the peninsula occurs at warmer brightness temperatures than would be expected from the Goddard profiling (GPROF) database. By incorporating the locally observed rain characteristics into the GPROF algorithm, efforts are made to test whether locally appropriate hydrometeor profiles may be used to improve the retrieved rainfall. Profiles are obtained by simulating rain cases using the cloud-resolving University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) model and matching the calculated radar reflectivities to TRMM precipitation radar (PR) reflectivities. Selected profiles and the corresponding simulated TMI brightness temperatures (limited in this study to values that are larger than 235 K) are added to the GPROF database to form a modified database that is considered to be more suitable for local application over the Korean Peninsula. The rainfall retrieved from the new database demonstrates that heavy-rainfall events—in particular, those associated with warmer clouds—are better captured by the new algorithm as compared with the official TRMM GPROF version-6 retrievals. The results suggest that a more locally suitable rain retrieval algorithm can be developed if locally representative rain characteristics are included in the GPROF algorithm.
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Prat, Olivier P., Ana P. Barros, and Christopher R. Williams. "An Intercomparison of Model Simulations and VPR Estimates of the Vertical Structure of Warm Stratiform Rainfall during TWP-ICE." Journal of Applied Meteorology and Climatology 47, no. 11 (November 1, 2008): 2797–815. http://dx.doi.org/10.1175/2008jamc1801.1.
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Abstract A model of rain shaft microphysics that solves the stochastic advection–coalescence–breakup equation in an atmospheric column was used to simulate the evolution of a stratiform rainfall event during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) in Darwin, Australia. For the first time, a dynamic simulation of the evolution of the drop spectra within a one-dimensional rain shaft is performed using realistic boundary conditions retrieved from real rain events. Droplet size distribution (DSD) retrieved from vertically pointing radar (VPR) measurements are sequentially imposed at the top of the rain shaft as boundary conditions to emulate a realistic rain event. Time series of model profiles of integral parameters such as reflectivity, rain rate, and liquid water content were subsequently compared with estimates retrieved from vertically pointing radars and Joss–Waldvogel disdrometer (JWD) observations. Results obtained are within the VPR retrieval uncertainty estimates. Besides evaluating the model’s ability to capture the dynamical evolution of the DSD within the rain shaft, a case study was conducted to assess the potential use of the model as a physically based interpolator to improve radar retrieval at low levels in the atmosphere. Numerical results showed that relative improvements on the order of 90% in the estimation of rain rate and liquid water content can be achieved close to the ground where the VPR estimates are less reliable. These findings raise important questions with regard to the importance of bin resolution and the lack of sensitivity for small raindrop size (<0.03 cm) in the interpretation of JWD data, and the implications of using disdrometer data to calibrate radar algorithms.
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Zhang, Ruanyu, Christian D. Kummerow, David L. Randel, Paula J. Brown, Wesley Berg, and Zhenzhan Wang. "Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)." Journal of Atmospheric and Oceanic Technology 36, no. 5 (May 2019): 849–64. http://dx.doi.org/10.1175/jtech-d-18-0167.1.
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AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.
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Aoki, Makoto, Hironori Iwai, Katsuhiro Nakagawa, Shoken Ishii, and Kohei Mizutani. "Measurements of Rainfall Velocity and Raindrop Size Distribution Using Coherent Doppler Lidar." Journal of Atmospheric and Oceanic Technology 33, no. 9 (September 2016): 1949–66. http://dx.doi.org/10.1175/jtech-d-15-0111.1.
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AbstractRainfall velocity, raindrop size distribution (DSD), and vertical wind velocity were simultaneously observed with 2.05- and 1.54-μm coherent Doppler lidars during convective and stratiform rain events. A retrieval method is based on identifying two separate spectra from the convolution of the aerosol and precipitation Doppler lidar spectra. The vertical wind velocity was retrieved from the aerosol spectrum peak and then the terminal rainfall velocity corrected by the vertical air motion from the precipitation spectrum peak was obtained. The DSD was derived from the precipitation spectrum using the relationship between the raindrop size and the terminal rainfall velocity. A comparison of the 1-min-averaged rainfall velocity from Doppler lidar measurements at a minimum range and that from a collocated ground-based optical disdrometer revealed high correlation coefficients of over 0.89 for both convective and stratiform rain events. The 1-min-averaged DSDs retrieved from the Doppler lidar spectrum using parametric and nonparametric methods are also in good agreement with those measured with the optical disdrometer with a correlation coefficient of over 0.80 for all rain events. To retrieve the DSD, the parametric method assumes a mathematical function for the DSD and the nonparametric method computes the direct deconvolution of the measured Doppler lidar spectrum without assuming a DSD function. It is confirmed that the Doppler lidar can retrieve the rainfall velocity and DSD during relatively heavy rain, whereas the ratio of valid data significantly decreases in light rain events because it is extremely difficult to separate the overlapping rain and aerosol peaks in the Doppler spectrum.
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Wu, Zuhang, Yun Zhang, Lifeng Zhang, Xiaolong Hao, Hengchi Lei, and Hepeng Zheng. "Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region." Water 11, no. 6 (June 16, 2019): 1260. http://dx.doi.org/10.3390/w11061260.
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In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based µ (3–5%) were proved to be the smallest in comparison with rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical Dm–Ze and Nw–Dm relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region.
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Tournadre, J. "Improved Level-3 Oceanic Rainfall Retrieval from Dual-Frequency Spaceborne Radar Altimeter Systems." Journal of Atmospheric and Oceanic Technology 23, no. 8 (August 1, 2006): 1131–49. http://dx.doi.org/10.1175/jtech1897.1.
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Abstract Since the launch of the Ocean Topography Experiment (TOPEX)/Poseidon in 1992, several studies have demonstrated that dual-frequency altimeter measurements cannot only accurately detect rain events but can also be used to infer quantitative values. The main problems with these techniques are the limited time and space sampling of nadir-looking instruments and the uncertainty in the height of the freezing level necessary to infer the surface rain rate from the measured signal attenuation. In addition to radar altimeters, altimetric satellites carry microwave radiometers designed to correct for atmospheric water effects. Using a radiative transfer model and simplified rainy atmospheres, a method of inversion of the microwave brightness temperatures in terms of freezing level is presented. The surface rain rate is then computed from the altimeter attenuation and the radiometer freezing level. The rain climatology is computed for the three altimeters currently in operation using a mixed lognormal distribution. Comparison with the Global Precipitation Climatology Project and Special Sensor Microwave Imager (SSM/I) climatologies shows that the use of freezing level greatly improves the altimeter climatology, which is of the same quality as that of the SSM/I for annual mean. The merging of the three altimeters is investigated. The resulting monthly mean rain rates are comparable to those derived from SSM/I. The high along-track resolution of altimeters also allows the determination of the length of rain events. The mean length is close to the SSM/I footprint size in the Tropics, but at higher latitude 80% of the rain has length scales smaller than 10 km, which might explain the relative underestimation of the mean rain rate by SSM/I.
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Huang, D., C. Zhao, M. Dunn, X. Dong, G. G. Mace, M. P. Jensen, S. Xie, and Y. Liu. "An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies." Atmospheric Measurement Techniques 5, no. 6 (June 25, 2012): 1409–24. http://dx.doi.org/10.5194/amt-5-1409-2012.
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Abstract. This paper presents a statistical comparison of three cloud retrieval products of the Atmospheric Radiation Measurement (ARM) program at the Southern Great Plains (SGP) site from 1998 to 2006: MICROBASE, University of Utah (UU), and University of North Dakota (UND) products. The probability density functions of the various cloud liquid water content (LWC) retrievals appear to be consistent with each other. While the mean MICROBASE and UU cloud LWC retrievals agree well in the middle of cloud, the discrepancy increases to about 0.03 gm−3 at cloud top and cloud base. Alarmingly large differences are found in the droplet effective radius (re) retrievals. The mean MICROBASE re is more than 6 μm lower than the UU re, whereas the discrepancy is reduced to within 1 μm if columns containing raining and/or mixed-phase layers are excluded from the comparison. A suite of stratified comparisons and retrieval experiments reveal that the LWC difference stems primarily from rain contamination, partitioning of total liquid later path (LWP) into warm and supercooled liquid, and the input cloud mask and LWP. The large discrepancy among the re retrievals is mainly due to rain contamination and the presence of mixed-phase layers. Since rain or ice particles are likely to dominate radar backscattering over cloud droplets, the large discrepancy found in this paper can be thought of as a physical limitation of single-frequency radar approaches. It is therefore suggested that data users should use the retrievals with caution when rain and/or mixed-phase layers are present in the column.
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Huang, Hao, Guifu Zhang, Kun Zhao, Su Liu, Long Wen, Gang Chen, and Zhengwei Yang. "Uncertainty in Retrieving Raindrop Size Distribution from Polarimetric Radar Measurements." Journal of Atmospheric and Oceanic Technology 36, no. 4 (April 2019): 585–605. http://dx.doi.org/10.1175/jtech-d-18-0107.1.
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AbstractDrop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (KDP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.
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Conrick, Robert, Joseph P. Zagrodnik, and Clifford F. Mass. "Dual-Polarization Radar Retrievals of Coastal Pacific Northwest Raindrop Size Distribution Parameters Using Random Forest Regression." Journal of Atmospheric and Oceanic Technology 37, no. 2 (February 2020): 229–42. http://dx.doi.org/10.1175/jtech-d-19-0107.1.
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AbstractRadar retrievals of drop size distribution (DSD) parameters are developed and evaluated over the mountainous Olympic Peninsula of Washington State. The observations used to develop retrievals were collected during the 2015/16 Olympic Mountain Experiment (OLYMPEX) and included the NASA S-band dual-polarimetric (NPOL) radar and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometers over the windward slopes of the barrier. Nonlinear and random forest regressions are applied to the PARSIVEL2 data to develop retrievals for median volume diameter, liquid water content, and rain rate. Improvement in DSD retrieval accuracy, defined by the mean error of the retrieval relative to PARSIVEL2 observations, was achieved when using the random forest model when compared with nonlinear regression. Evaluation of disdrometer observations and the retrievals from NPOL indicate that the radar retrievals can accurately reproduce observed DSDs in this region, including the common wintertime regime of small but numerous raindrops that is important there. NPOL retrievals during the OLYMPEX period are further evaluated using two-dimensional video disdrometers (2DVD) and vertically pointing Micro Rain Radars. Results indicate that radar retrievals using random forests may be skillful in capturing DSD characteristics in the lowest portions of the atmosphere.
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Battaglia, Alessandro, Pablo Saavedra, Thomas Rose, and Clemens Simmer. "Characterization of Precipitating Clouds by Ground-Based Measurements with the Triple-Frequency Polarized Microwave Radiometer ADMIRARI." Journal of Applied Meteorology and Climatology 49, no. 3 (March 1, 2010): 394–414. http://dx.doi.org/10.1175/2009jamc2340.1.
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Abstract A groundbreaking new-concept multiwavelength dual-polarized Advanced Microwave Radiometer for Rain Identification (ADMIRARI) has been built and continuously operated in two field campaigns: the Convective and Orographically Induced Precipitation Study (COPS) and the European Integrated Project on Aerosol Cloud Climate Air Quality Interactions (EUCAARI). The radiometer has 6 channels working in horizontal and vertical polarization at 10.65, 21.0, and 36.5 GHz, and it is completely steerable both in azimuth and in elevation. The instrument is suited to be operated in rainy conditions and is intended for retrieving simultaneously water vapor, rain, and cloud liquid water paths. To this goal the authors implemented a Bayesian retrieval scheme based on many state realizations simulated by the Goddard Cumulus Ensemble model that build up a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations, which account for the presence of nonspherical particles in preferential orientation, simulate the downwelling brightness temperatures and establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. Particular attention is devoted to the sensitivity of the ADMIRARI signal to 3D effects, raindrop size distribution, and axial ratio parameterizations. The polarization and multifrequency signals represent key information to separate the effects introduced by non-Rayleigh scatterers and to separate rainwater (r-LWP) from the cloud water component (c-LWP). Long-term observations demonstrate that observed brightness temperatures and polarization differences can be well interpreted and reproduced by the simulated ones for all three channels simultaneously. Rough estimates of r-LWP derived from collocated observations with a micro rain radar confirm the rain/no rain separation and the variability trend of r-LWP provided by the radiometer-based retrieval algorithm. With this work the authors demonstrate the potential of ADMIRARI to retrieve information about the rain/cloud partitioning for midlatitude precipitation systems; future studies with this instrument will provide crucial information on rain efficiency of clouds for cloud modelers that might lead toward a better characterization of rain processes.
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Liao, Liang, and Robert Meneghini. "A Modified Dual-Wavelength Technique for Ku- and Ka-Band Radar Rain Retrieval." Journal of Applied Meteorology and Climatology 58, no. 1 (January 2019): 3–18. http://dx.doi.org/10.1175/jamc-d-18-0037.1.
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AbstractTo overcome a deficiency in the standard Ku- and Ka-band dual-wavelength radar technique, a modified version of the method is introduced. The deficiency arises from ambiguities in the estimate of the mass-weighted diameter Dm of the raindrop size distribution (DSD) derived from the differential frequency ratio (DFR), defined as the difference between the radar reflectivity factors (dB) at Ku and Ka band ZKu − ZKa. In particular, for DFR values less than zero, there are two possible solutions of Dm, leading to ambiguities in the retrieved DSD parameters. It is shown that the double solutions to Dm are effectively eliminated if the DFR is modified from ZKu − ZKa to ZKu − γZKa (dB), where γ is a constant with a value less than 0.8. An optimal radar algorithm that uses the modified DFR for the retrieval of rain and Dm profiles is described. The validity and accuracy of the algorithm are tested by applying it to radar profiles that are generated from measured DSD data. Comparisons of the rain rates and Dm estimated from the modified DFR algorithm to the same hydrometeor quantities computed directly from the DSD spectra (or the truth) indicate that the modified DFR-based profiling retrievals perform fairly well and are superior in accuracy and robustness to retrievals using the standard DFR.
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Matrosov, Sergey Y., Peter T. May, and Matthew D. Shupe. "Rainfall Profiling Using Atmospheric Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars." Journal of Atmospheric and Oceanic Technology 23, no. 11 (November 1, 2006): 1478–91. http://dx.doi.org/10.1175/jtech1957.1.
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Abstract An attenuation-based method to retrieve vertical profiles of rainfall rate from vertically pointing Ka-band radar measurements has been refined and adjusted for use with the U.S. Department of Energy’s cloud radars deployed at multiple Atmospheric Radiation Program (ARM) test bed sites. This method takes advantage of the linear relationship between the rainfall rate and the attenuation coefficient, and can account for a priori information about the vertical profile of nonattenuated reflectivity. The retrieval method is applied to a wide variety of rainfall events observed at different ARM sites ranging from stratiform events with low-to-moderate rainfall rates (∼5 mm h−1) to heavy convective rains with rainfall rates approaching 100 mm h−1. The Ka-band attenuation-based retrieval results expressed in both instantaneous rainfall rates and in rainfall accumulations are compared to available surface data and measurements of a scanning C-band precipitation polarimetric radar located near the Darwin, Australia, ARM test bed site. The Ka-band retrievals are found to be in good agreement with the C-band radar estimates, which are based both on conventional radar reflectivity approaches and on polarimetric differential phase shift measurements. Typically, the C-band–Ka-band radar estimate differences are within the expected retrieval uncertainties. The magnitude of the Ka-band rainfall-rate estimate error depends on the retrieval resolution, rain intensity, and uncertainties in the profiles of nonattenuated reflectivity. It is shown that reasonable retrieval accuracies (∼15%–40%) can be achieved for a large dynamic range of observed rainfall rates (4–100 mm h−1) and the effective vertical resolution of about 1 km. The potential enhancements of the Ka-band attenuation-based method by including a priori information on vertical profiles of nonattenuated reflectivity and increasing the height range of the retrievals by using Ka-band polarization measurements are also discussed. The addition of the precipitation products to the suite of ARM hydrometeor retrievals can enhance the overall characterization of the vertical atmospheric column.
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Unal, Christine. "High-Resolution Raindrop Size Distribution Retrieval Based on the Doppler Spectrum in the Case of Slant Profiling Radar." Journal of Atmospheric and Oceanic Technology 32, no. 6 (June 2015): 1191–208. http://dx.doi.org/10.1175/jtech-d-13-00225.1.
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AbstractDoppler spectra from vertically profiling radars are usually considered to retrieve the raindrop size distribution (DSD). However, to exploit both fall velocity spectrum and polarimetric measurements, Doppler spectra acquired in slant profiling mode should be explored. Rain DSD samples are obtained from simultaneously measured vertical and slant profile Doppler spectra and evaluated. In particular, the effect of the horizontal wind and the averaging time are investigated.The Doppler spectrum provides a way to retrieve the DSD, the radial wind, and a spectral broadening factor by means of a nonlinear optimization technique. For slant profiling of light rain when the horizontal wind is strong, the DSD results can be affected. Such an effect is demonstrated on a study case of stratiform light rain. Adding a wind profiler mode to the radar simultaneously supplies the horizontal wind and Doppler spectra. Before the retrieval procedure, the Doppler spectra are shifted in velocity to remove the mean horizontal wind contribution. The DSD results are considerably improved.Generally, averaged Doppler spectra are input into this type of algorithm. Instead, high-resolution, low-averaged Doppler spectra are chosen in order to take into account the small-scale variability of the rainfall. Investigating the linear relations at fixed median volume diameter, measured reflectivity-retrieved rainfall rate, for a slant beam, the consistency of the integrated parameters is established for two averaging periods. Nevertheless, the corresponding DSD parameter distributions reveal differences attributed to the averaging of the Doppler spectra.The new aspects are to obtain the same retrieval quality as vertically profiling and highly averaged spectra in an automated way.
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Prabhakara, C., R. Meneghini, D. A. Short, J. A. Weinman, R. Jr Iacovazzi, R. Oki, and M. Cadeddu. "A TRMM Microwave Radiometer Rain Retrieval Method Based on Fractional Rain Area." Journal of the Meteorological Society of Japan. Ser. II 76, no. 5 (1998): 765–81. http://dx.doi.org/10.2151/jmsj1965.76.5_765.
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Loh, Jui Le, Dong-In Lee, Mi-Young Kang, and Cheol-Hwan You. "Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea." Remote Sensing 12, no. 4 (February 14, 2020): 642. http://dx.doi.org/10.3390/rs12040642.
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Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.
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Sapp, Joseph, Suleiman Alsweiss, Zorana Jelenak, Paul Chang, and James Carswell. "Stepped Frequency Microwave Radiometer Wind-Speed Retrieval Improvements." Remote Sensing 11, no. 3 (January 22, 2019): 214. http://dx.doi.org/10.3390/rs11030214.
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With the operational deployment of the *SFMR, hurricane reconnaissance and research aircraft provide near real-time observations of the 10 m ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate *SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the *SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 m / s –45 m / s with respect to *GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45/ h at tropical storm- and hurricane-force speeds present in the current operational model. We also update the *SFMR *RTM to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current *SFMR calibration procedures are required.
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Alasgah, Abdusalam, Maria Jacob, Linwood Jones, and Larry Schneider. "Validation of the Hurricane Imaging Radiometer Forward Radiative Transfer Model for a Convective Rain Event." Remote Sensing 11, no. 22 (November 13, 2019): 2650. http://dx.doi.org/10.3390/rs11222650.
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The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM.