scholarly journals Warm Rain Rates from AMSR-E 89-GHz Brightness Temperatures Trained Using CloudSat Rain-Rate Observations

2019 ◽  
Vol 36 (6) ◽  
pp. 1033-1051 ◽  
Author(s):  
Ryan Eastman ◽  
Matthew Lebsock ◽  
Robert Wood
Keyword(s):  
Rain Rate ◽  
Sea Surface ◽  
Training Dataset ◽  
Light Rain ◽  
Brightness Temperatures ◽  
Warm Rain ◽  
Confounding Variables ◽  
The Mean ◽  
Subtropical Oceans ◽  
Rain Rates

AbstractCollocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km × 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics.

scholarly journals Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

2019 ◽  
Vol 36 (5) ◽  
pp. 849-864 ◽  
Author(s):  
Ruanyu Zhang ◽  
Christian D. Kummerow ◽  
David L. Randel ◽  
Paula J. Brown ◽  
Wesley Berg ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
A Priori ◽  
Dual Frequency ◽  
Ancillary Data ◽  
Light Rain ◽  
Brightness Temperatures ◽  
The Mean ◽  
Microwave Imager ◽  
Rain Rates ◽  
Global Precipitation

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.

scholarly journals Improved rain-rate and drop-size retrievals from airborne and spaceborne Doppler radar

2017 ◽  
Author(s):  
Shannon L. Mason ◽  
J. Christine Chiu ◽  
Robin J. Hogan ◽  
Lin Tian
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Rain Rate ◽  
Doppler Radar ◽  
Number Concentration ◽  
Doppler Velocity ◽  
Light Rain ◽  
Warm Rain ◽  
Rain Drop ◽  
Drop Number

Abstract. Satellite radar remote-sensing of rain is important for quantifying of the global hydrological cycle, atmospheric energy budget, and many microphysical cloud and precipitation processes; however, radar estimates of rain rate are sensitive to assumptions about the raindrop size distribution. The upcoming EarthCARE satellite will feature a 94-GHz Doppler radar alongside lidar and radiometer instruments, presenting opportunities for enhanced global retrievals of the rain drop size distribution. In this paper we demonstrate the capability to retrieve both rain rate and a parameter of the rain drop size distribution from an airborne 94-GHz Doppler radar using CAPTIVATE, the variational retrieval algorithm developed for EarthCARE radar–lidar synergy. For a range of rain regimes observed during the Tropical Composition, Cloud and Climate Coupling (TC4) field campaign in the eastern Pacific in 2007, we explore the contributions of Doppler velocity and path-integrated attenuation (PIA) to the retrievals, and evaluate the retrievals against independent measurements from a second, less attenuated, Doppler radar aboard the same aircraft. Retrieved drop number concentration varied over five orders of magnitude between light rain from melting ice, and warm rain from liquid clouds. Doppler velocity can be used to estimate rain rate over land, and retrievals of rain rate and drop number concentration are possible in profiles of light rain over land; in moderate warm rain, drop number concentration can be retrieved without Doppler velocity. These results suggest that EarthCARE rain retrievals facilitated by Doppler radar will make substantial improvements to the global understanding of the interaction of clouds and precipitation.

scholarly journals A comparison of rainfall measurements from multiple instruments

2013 ◽  
Vol 6 (7) ◽  
pp. 1585-1595 ◽  
Author(s):  
X. C. Liu ◽  
T. C. Gao ◽  
L. Liu
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Measurement Range ◽  
Light Rain ◽  
Large Size ◽  
Drop Size Distributions ◽  
Rain Rates ◽  
Effect Of Light ◽  
Good Agreement

Abstract. Simultaneous observations of rainfall collected by a tipping bucket rain gauge (TBRG), a weighing rain gauge (WRG), an optical rain gauge (ORG), a present weather detector (PWD), a Joss–Waldvogel disdrometer (JWD), and a 2-D video disdrometer (2DVD) during January to October 2012 were analyzed to evaluate how accurately they measure rainfall and drop size distributions (DSDs). For the long-term observations, there were different discrepancies in rain amounts from six instruments on the order of 0% to 27.7%. The TBRG, WRG, and ORG have a good agreement, while the PWD and 2DVD record higher and the JWD lower rain rates when R > 20 mm h−1, the ORG agrees well with JWD and 2DVD, while the TBRG records higher and the WRG lower rain rates when R > 20 mm h−1. Compared with the TBRG and WRG, optical and impact instruments can measure the rain rate accurately in the light rain. The overall DSDs of JWD and 2DVD agree well with each other, except for the small raindrops (D < 1 mm). JWD can measure more moderate-size raindrops (0.3 mm < D < 1.5 mm) than 2DVD, but 2DVD can measure more small-size raindrops (D < 0.3 mm). 2DVD has a larger measurement range; more overall raindrops can be measured by 2DVD than by JWD in different rain rate regimes. But small raindrops might be underestimated by 2DVD when R > 15 mm h−1. The small raindrops tend to be omitted in the more large-size raindrops due to the shadow effect of light. Therefore, the measurement accuracy of small raindrops in the heavy rainfall from 2DVD should be handled carefully.

Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent

2017 ◽  
Vol 56 (7) ◽  
pp. 1867-1881 ◽  
Author(s):  
Andung Bayu Sekaranom ◽  
Hirohiko Masunaga
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Maritime Continent ◽  
Brightness Temperatures ◽  
Extreme Rain ◽  
Trmm Precipitation ◽  
Precipitation Analysis ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates

AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.

The Distribution of Rainfall over Oceans from Spaceborne Radars

2010 ◽  
Vol 49 (3) ◽  
pp. 535-543 ◽  
Author(s):  
Wesley Berg ◽  
Tristan L’Ecuyer ◽  
John M. Haynes
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Precipitable Water ◽  
Rainfall Regime ◽  
Tropical Regions ◽  
Light Rain ◽  
Convective Storms ◽  
Relative Contribution ◽  
Rainfall Frequency ◽  
Subtropical Oceans ◽  
Rain Rates

Abstract A combination of rainfall estimates from the 13.8-GHz Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the 94-GHz CloudSat Cloud Profiling Radar (CPR) is used to assess the distribution of rainfall intensity over tropical and subtropical oceans. These two spaceborne radars provide highly complementary information: the PR provides the best information on the total rain volume because of its ability to estimate the intensity of all but the lightest rain rates while the CPR’s higher sensitivity provides superior rainfall detection as well as estimates of drizzle and light rain. Over the TRMM region between 35°S and 35°N, rainfall frequency from the CPR is around 9%, approximately 2.5 times that detected by the PR, and the CPR estimates indicate a contribution by light rain that is undetected by the PR of around 10% of the total. Stratifying the results by total precipitable water (TPW) as a proxy for rainfall regime indicates dramatic differences over stratus-dominated subsidence regions, with nearly 20% of the total rain occurring as light rain. Over moist tropical regions, the CPR substantially underestimates rain from intense convective storms because of large attenuation and multiple-scattering effects while the PR misses very little of the total rain volume because of a lower relative contribution from light rain. Over low-TPW regions, however, inconsistencies between estimates from the PR and the CPR point to uncertainties in the algorithm assumptions that remain to be understood and addressed.

scholarly journals Automated Identification of Enhanced Rainfall Rates Using the Near-Storm Environment for Radar Precipitation Estimates

2014 ◽  
Vol 15 (3) ◽  
pp. 1238-1254 ◽  
Author(s):  
Heather M. Grams ◽  
Jian Zhang ◽  
Kimberly L. Elmore
Keyword(s):  
Rain Rate ◽  
Flash Flood ◽  
Automated Identification ◽  
Freezing Level ◽  
Warm Rain ◽  
Basin Scale ◽  
Precipitation Estimates ◽  
Importance Analysis ◽  
Two Parameters ◽  
Rain Rates

Abstract Reliable and timely flash flood warnings are critically dependent on the accuracy of real-time rainfall estimates. Precipitation is not only the most vital input for basin-scale accumulation algorithms such as the Flash Flood Monitoring and Prediction (FFMP) program used operationally by the U.S. National Weather Service, but it is the primary forcing for hydrologic models at all scales. Quantitative precipitation estimates (QPE) from radar are widely used for such a purpose because of their high spatial and temporal resolution. However, converting the native radar variables into an instantaneous rain rate is fraught with challenges and uncertainties. This study addresses the challenge of identifying environments conducive for tropical rain rates, or rain rates that are enhanced by highly productive warm rain growth processes. Model analysis fields of various thermodynamic and moisture parameters were used as predictors in a decision tree–based ensemble to generate probabilities of warm rain–dominated drop growth. Variable importance analysis from the ensemble training showed that the probability accuracy was most dependent on two parameters in particular: freezing-level height and lapse rates of temperature. The probabilities were used to assign a tropical rain rate for hourly QPE and were evaluated against existing Z–R–based QPE products available to forecasters. The probability-based delineations showed improvement in QPE over the existing methods, but the two predictands tested had varying levels of performance for the storm types evaluated and require further study.

scholarly journals Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain

2012 ◽  
Vol 9 (5) ◽  
pp. 3331-3357 ◽  
Author(s):  
J. Boutin ◽  
N. Martin ◽  
G. Reverdin ◽  
X. Yin ◽  
F. Gaillard
Keyword(s):  
European Space Agency ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Stratification ◽  
Space Agency ◽  
Ocean Salinity ◽  
The Mean ◽  
Rain Rates

Abstract. The sea surface salinity (SSS) measured from space by the Soil Moisture and Ocean Salinity (SMOS) mission has recently been revisited by the European Space Agency first campaign reprocessing. We show that, with respect to the previous version, biases close to land and ice greatly decrease. The accuracy of SMOS SSS averaged over 10 days 100 × 100 km2 in the open ocean and estimated by comparison to ARGO SSS is on the order of 0.3–0.4 in tropical and subtropical regions and 0.5 in a cold region. The mean SSS −0.1 bias observed in the Tropical Pacific Ocean between 5° N and 15° N, relatively to other regions, is suppressed when SMOS rainy events, as detected on SSMIs rain rates, are removed from the SMOS-ARGO comparisons. The SMOS freshening is linearly correlated to SSMIs rain rate with a slope estimated to −0.14 mm−1 h, after correction for rain atmospheric contribution. This tendency is the signature of the temporal SSS variability between the time of SMOS and ARGO measurements linked to rain variability and of the vertical salinity stratification between the first centimeter of the sea surface layer sampled by SMOS and the 5 m depth sampled by ARGO. However, given that the whole set of collocations includes situations with rainy ARGO measurements collocated with non rainy SMOS measurements, the mean −0.1 bias and the negative skewness of the statistical distribution of SMOS minus ARGO SSS difference are very likely the mean signature of the vertical salinity stratification. In the future, the analysis of ongoing in situ salinity measurements in the top 50 cm of the sea surface and of Aquarius satellite SSS are expected to provide complementary information about the sea surface salinity stratification.

scholarly journals Polarimetric Estimates of a 1-Month Accumulation of Light Rain with a 3-cm Wavelength Radar

2011 ◽  
Vol 12 (5) ◽  
pp. 1024-1039 ◽  
Author(s):  
L. Borowska ◽  
D. Zrnić ◽  
A. Ryzhkov ◽  
P. Zhang ◽  
C. Simmer
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Light Rain ◽  
Differential Phase ◽  
Rain Gauges ◽  
Bright Band ◽  
X Band ◽  
Wet Snow ◽  
Composite Estimator ◽  
Rain Rates

Abstract The authors evaluate rainfall estimates from the new polarimetric X-band radar at Bonn, Germany, for a period between mid-November and the end of December 2009 by comparison with rain gauges. The emphasis is on slightly more than 1-month accumulations over areas minimally affected by beam blockage. The rain regime was characterized by reflectivities mainly below 45 dBZ, maximum observed rain rates of 47 mm h−1, a mean rain rate of 0.1 mm h−1, and brightband altitudes between 0.6 and 2.4 km above the ground. Both the reflectivity factor and the specific differential phase are used to obtain the rain rates. The accuracy of rain total estimates is evaluated from the statistics of the differences between radar and rain gauge measurements. Polarimetry provides improvement in the statistics of reflectivity-based measurements by reducing the bias and RMS errors from −25% to 7% and from 33% to 17%, respectively. Essential to this improvement is separation of the data into those attributed to pure rain, those from the bright band, and those due to nonmeteorological scatterers. A type-specific (rain or wet snow) relation is applied to obtain the rain rate by matching on the average the contribution by wet snow to the radar-measured rainfall below the bright band. The measurement of rain using specific differential phase is the most robust and can be applied to the very low rain rates and still produce credible accumulation estimates characterized with a standard deviation of 11% but a bias of −25%. A composite estimator is also tested and discussed.

Estimation of Rain Rate from Airborne Doppler W-Band Radar in CalWater-2

2018 ◽  
Vol 35 (3) ◽  
pp. 593-608 ◽  
Author(s):  
C. W. Fairall ◽  
Sergey Y. Matrosov ◽  
Christopher R. Williams ◽  
E. J. Walsh
Keyword(s):  
Rain Rate ◽  
Microwave Radiometer ◽  
Hybrid Approach ◽  
Precipitation Rate ◽  
Doppler Velocity ◽  
Surface Boundary ◽  
Radar Altimeter ◽  
Light Rain ◽  
W Band ◽  
Rain Rates

ABSTRACTThe NOAA W-band radar was deployed on a P-3 aircraft during a study of storm fronts off the U.S. West Coast in 2015 in the second CalWater (CalWater-2) field program. This paper presents an analysis of measured equivalent radar reflectivity factor Zem profiles to estimate the path-averaged precipitation rate and profiles of precipitation microphysics. Several approaches are explored using information derived from attenuation of Zem as a result of absorption and scattering by raindrops. The first approach uses the observed decrease of Zem with range below the aircraft to estimate column mean precipitation rates. A hybrid approach that combines Zem in light rain and attenuation in stronger rain performed best. The second approach estimates path-integrated attenuation (PIA) via the difference in measured and calculated normalized radar cross sections (NRCSm and NRCSc, respectively) retrieved from the ocean surface. The retrieved rain rates are compared to estimates from two other systems on the P-3: a Stepped Frequency Microwave Radiometer (SFMR) and a Wide-Swath Radar Altimeter (WSRA). The W-band radar gives reasonable values for rain rates in the range 0–10 mm h−1 with an uncertainty on the order of 1 mm h−1. Mean profiles of Zem, raindrop Doppler velocity, attenuation, and precipitation rate in bins of rain rate are also computed. A method for correcting measured profiles of Zem for attenuation to estimate profiles of nonattenuated profiles of Ze is examined. Good results are obtained by referencing the surface boundary condition to the NRCS values of PIA. Limitations of the methods are discussed.

Intercomparisons of CloudSat and Ground-Based Radar Retrievals of Rain Rate over Land

2014 ◽  
Vol 53 (10) ◽  
pp. 2360-2370 ◽  
Author(s):  
Sergey Y. Matrosov
Keyword(s):  
Rain Rate ◽  
Correlation Coefficients ◽  
Stratiform Precipitation ◽  
Radar Measurements ◽  
The Mean ◽  
Satellite Radar ◽  
Mean Differences ◽  
Radar Rain Rate ◽  
Rain Rates ◽  
Precipitation Events

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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