Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land Using Statistical Information for Brightness Temperatures under No-Rain Conditions

2005 ◽  
Vol 44 (8) ◽  
pp. 1243-1259 ◽  
Author(s):  
Shinta Seto ◽  
Nobuhiro Takahashi ◽  
Toshio Iguchi
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Regression Equation ◽  
Retrieval Algorithm ◽  
Vertical Polarization ◽  
Surface Conditions ◽  
Background Brightness

Abstract One of the goals of the Global Precipitation Measurement project, the successor to the Tropical Rainfall Measuring Mission (TRMM), is to produce a 3-hourly global rainfall map using several spaceborne microwave radiometers. It is important, although often difficult, to classify radiometer observations over land as either “rain” or “no rain” because background land surface conditions change significantly with time and location. In this study, a no-rain brightness temperature database was created to infer land surface conditions using simultaneous observations by TRMM Microwave Imager (TMI) and precipitation radar (PR) with a resolution of 1 month and 1° latitude × 1° longitude. This paper proposes new rain/no-rain classification (RNC) methods that use the database to determine the background brightness temperature. The proposed RNC methods and the RNC method developed for the Goddard profiling algorithm (GPROF; the standard rain-rate retrieval algorithm for TMI) are applied to all TMI observations for the entire year of 2000, and the results are evaluated against the RNC made by PR as the “truth.” The first method (M1) simply uses the average brightness temperature at 85-GHz vertical polarization [denoted as TB (85 V)] under no-rain conditions as the background brightness temperature at 85-GHz vertical polarization [denoted as TBe (85 V)]. The second method (M2) uses a regression equation between TB (85 V) and TB (22 V) under no-rain conditions from the database. Here, TBe (85 V) is calculated by substituting the observed TB (22 V) into the regression equation. The ratio of accurate rain detection by GPROF to all rain occurrences detected by PR was 59%. This ratio was 57% for M1 and 63% for M2. The ratio with the weight of the rain rate was 81% for M1 and 86% for M2; it was 80% for GPROF. These comparisons were made by setting a threshold using a constant coefficient k0 to make the ratio of false rain detection to all no-rain occurrences detected by PR almost the same (approximately 0.85%) for all three methods. Further comparisons among the methods are made, and the reasons for the differences are investigated herein.

Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land

2008 ◽  
Vol 47 (11) ◽  
pp. 3016-3029 ◽  
Author(s):  
Shinta Seto ◽  
Takuji Kubota ◽  
Nobuhiro Takahashi ◽  
Toshio Iguchi ◽  
Taikan Oki
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Retrieval Algorithm ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

scholarly journals Satellite Microwave Surface Observations in Tropical Cyclones

2010 ◽  
Vol 138 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Yves Quilfen ◽  
Bertrand Chapron ◽  
Jean Tournadre
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Surface Wind Speed ◽  
Microwave Measurements ◽  
Dual Frequency

Abstract Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.

scholarly journals A Comparison of Gamma and Lognormal Distributions for Characterizing Satellite Rain Rates from the Tropical Rainfall Measuring Mission

2004 ◽  
Vol 43 (11) ◽  
pp. 1586-1597 ◽  
Author(s):  
Hye-Kyung Cho ◽  
Kenneth P. Bowman ◽  
Gerald R. North
Keyword(s):  
Gamma Distribution ◽  
Lognormal Distribution ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Retrieval Algorithm ◽  
Sample Mean ◽  
Tropical Rainfall ◽  
Lognormal Distributions ◽  
Rain Rates

Abstract This study investigates the spatial characteristics of nonzero rain rates to develop a probability density function (PDF) model of precipitation using rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The minimum χ2 method is used to find a good estimator for the rain-rate distribution between the gamma and lognormal distributions, which are popularly used in the simulation of the rain-rate PDF. Results are sensitive to the choice of dynamic range, but both the gamma and lognormal distributions match well with the PDF of rainfall data. Comparison with sample means shows that the parametric mean from the lognormal distribution overestimates the sample mean, whereas the gamma distribution underestimates it. These differences are caused by the inflated tail in the lognormal distribution and the small shape parameter in the gamma distribution. If shape constraint is given, the difference between the sample mean and the parametric mean from the fitted gamma distribution decreases significantly, although the resulting χ2 values slightly increase. Of interest is that a consistent regional preference between two test functions is found. The gamma fits outperform the lognormal fits in wet regions, whereas the lognormal fits are better than the gamma fits for dry regions. Results can be improved with a specific model assumption depending on mean rain rates, but the results presented in this study can be easily applied to develop the rainfall retrieval algorithm and to find the proper statistics in the rainfall data.

Rain Retrieval from TMI Brightness Temperature Measurements Using a TRMM PR–Based Database

2006 ◽  
Vol 45 (3) ◽  
pp. 455-466 ◽  
Author(s):  
Nicolas Viltard ◽  
Corinne Burlaud ◽  
Christian D. Kummerow
Keyword(s):  
Brightness Temperature ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
High Standard ◽  
Brightness Temperatures ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Point To Point ◽  
Trmm Microwave Imager ◽  
Test Database

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.

Improving Precipitation Retrieval by Brightness Temperature Temporal Variation (ΔTB): Definition, Computation, and Application

2021 ◽  
Author(s):  
Yalei You ◽  
Christa Peters-Lidard ◽  
Stephen Munchak ◽  
Sarah Ringerud
Keyword(s):  
Temporal Variation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Daily Rainfall ◽  
Surface Emissivity ◽  
Time Dimension ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

<p>Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) from a single satellite to estimate the precipitation rate. This study proposed to add the time-dimension into the precipitation estimation process by using the TB (or emissivity) temporal variation (ΔTB or Δe) derived from the Global Precipitation Measurement (GPM) microwave radiometer constellation.  Results showed that (1) ΔTB can improve the precipitation estimation over the cold surfaces (i.e., snow-covered region) through minimizing the microwave land surface emissivity’s influence; (2) Δe under the clear-sky conditions can accurately estimate the daily rainfall accumulation; and (3) ΔTB can be used to identify the liquid raindrop signature over the low surface emissivity areas. This study highlights the importance of maintaining the current passive microwave satellite constellation.</p>

A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers

2015 ◽  
Vol 32 (10) ◽  
pp. 1866-1879 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Single Column ◽  
Simplifying Assumptions ◽  
Cross Track

AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

scholarly journals Constructing a Multifrequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain

2019 ◽  
Vol 58 (9) ◽  
pp. 1889-1904 ◽  
Author(s):  
Sarah D. Bang ◽  
Daniel J. Cecil
Keyword(s):  
United States ◽  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Low Earth Orbit ◽  
Passive Microwave ◽  
Central United States ◽  
Microwave Data

AbstractLarge hail is a primary contributor to damages and loss around the world, in both agriculture and infrastructure. The sensitivity of passive microwave radiometer measurements to scattering by hail led to the development of proxies for severe hail, most of which use brightness temperature thresholds from 37-GHz and higher-frequency microwave channels on board weather satellites in low-Earth orbit. Using 16+ years of data from the Tropical Rainfall Measuring Mission (TRMM; 36°S–36°N), we pair TRMM brightness temperature–derived precipitation features with surface hail reports in the United States to train a hail retrieval on passive microwave data from the 10-, 19-, 37-, and 85-GHz channels based on probability curves fit to the microwave data. We then apply this hail retrieval to features in the Global Precipitation Measurement (GPM) domain (from 69°S to 69°N) to develop a nearly global passive microwave–based climatology of hail. The extended domain of the GPM satellite into higher latitudes requires filtering out features that we believe are over icy and snowy surface regimes. We also normalize brightness temperature depression by tropopause height in an effort to account for differences in storm depth between the tropics and higher latitudes. Our results show the highest hail frequencies in the region of northern Argentina through Paraguay, Uruguay, and southern Brazil; the central United States; and a swath of Africa just south of the Sahel. Smaller hot spots include Pakistan, eastern India, and Bangladesh. A notable difference between these results and many prior satellite-based studies is that central Africa, while still active in our climatology, does not rival the aforementioned regions in retrieved hailstorm frequency.

Rainfall-Induced Changes in Actual Surface Backscattering Cross Sections and Effects on Rain-Rate Estimates by Spaceborne Precipitation Radar

2007 ◽  
Vol 24 (10) ◽  
pp. 1693-1709 ◽  
Author(s):  
Shinta Seto ◽  
Toshio Iguchi
Keyword(s):  
Cross Sections ◽  
Land Surface ◽  
Rain Rate ◽  
Incident Angle ◽  
Monthly Rainfall ◽  
Retrieval Algorithm ◽  
Precipitation Radar ◽  
Actual Surface ◽  
Spatial Reference ◽  
Trmm Pr

Abstract In this study, the authors used Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) data to investigate changes in the actual (attenuation corrected) surface backscattering cross section (σ0e) due to changes in surface conditions induced by rainfall, the effects of changes in σ0e on the path integrated attenuation (PIA) estimates by surface reference techniques (SRTs), and the effects on rain-rate estimates by the TRMM PR standard rain-rate retrieval algorithm. Over land, σ0e is statistically higher under rainfall than under no rainfall conditions (soil moisture effect) unless the land surface is densely covered by vegetation. Over ocean, the dependence of σ0e on the incident angle differs under rainfall and no-rainfall conditions (wind speed effect). The alongtrack spatial reference (ATSR) method, one of the SRTs used in the standard algorithm, partially considers these effects, while the temporal reference (TR) method, another SRT, never involves these effects; its PIA estimates thus have negative biases over land. In the hybrid spatial reference (HSR) method used over ocean, different incident angles create different biases in PIA estimates. If the TR method is replaced by the ATSR method, the monthly rainfall amount in July 2001 all over the land within the TRMM coverage increases by 0.70%. The bias in the HSR method over ocean can be mitigated by fitting a σ0–θ curve separately to smaller incident angles and to larger incident angles. This improvement increases or decreases the monthly rainfall amounts in individual incident angle regions by up to 10%.

scholarly journals Rain-Rate Characteristics over the Korean Peninsula and Improvement of the Goddard Profiling (GPROF) Database for TMI Rainfall Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 786-798 ◽  
Author(s):  
Geun-Hyeok Ryu ◽  
Byung-Ju Sohn ◽  
Christian D. Kummerow ◽  
Eun-Kyoung Seo ◽  
Gregory J. Tripoli
Keyword(s):  
Korean Peninsula ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Summer Rainfall ◽  
Retrieval Algorithm ◽  
Rain Gauges ◽  
Brightness Temperatures ◽  
University Of Wisconsin ◽  
Microwave Imager ◽  
Trmm Microwave Imager

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.

Effects of TRMM Boost on Oceanic Rainfall Estimates Based on Microwave Emission Brightness Temperature Histograms (METH)

2008 ◽  
Vol 25 (10) ◽  
pp. 1888-1893 ◽  
Author(s):  
Dong-Bin Shin ◽  
Long S. Chiu
Keyword(s):  
Brightness Temperature ◽  
Correction Factor ◽  
Rain Rate ◽  
Incidence Angle ◽  
Tropical Rainfall Measuring Mission ◽  
Microwave Emission ◽  
Freezing Level ◽  
The Difference ◽  
Microwave Imager ◽  
Rain Rates

Abstract A physical–statistical algorithm for estimating space–time average oceanic rainfall has been applied to microwave measurements taken by the Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on board the TRMM satellite. The algorithm is based on Microwave Emission Brightness Temperature Histograms (METH) and produces monthly rainfall over 5° × 5° latitude–longitude boxes as a TRMM standard product (3A11). The TRMM satellite was boosted from an altitude of 350–402 km in August 2001 to extend its mission life. The orbit boost affected the orbital parameters, rain-rate–brightness temperature relations, and then rain-rate parameters. Using oceanic rain rates derived from SSM/I, the difference between 3A11 and SSM/I for the preboost and postboost periods was analyzed. The difference shows a significant jump from the preboost to the postboost data if no adjustments were made for the postboost TRMM data. The jumps in rain-rate parameters are attributed to the changes in earth’s incidence angle of TMI, affecting the brightness temperature in the TMI channels, the retrieved altitude of the freezing level, and the beam-filling correction factor. The changes in the brightness temperature (and freezing level) estimates and the beam-filling correction factor accounted for differences of approximately 4.9% and 1.5%, respectively. After the orbital and radiometric parameters are corrected for the boost, there is no detectable jump between the pre- and postboost 3A11 rain rates. The intersatellite calibration results demonstrate the robustness of the technique in producing a long record of climate-scale oceanic rainfall.

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