The Tropical Rainfall Potential (TRaP) Technique. Part II: Validation

2005 ◽  
Vol 20 (4) ◽  
pp. 465-475 ◽  
Author(s):  
Ralph Ferraro ◽  
Paul Pellegrino ◽  
Michael Turk ◽  
Wanchun Chen ◽  
Shuang Qiu ◽  
...  
Keyword(s):  
Information Service ◽  
Storm Track ◽  
Stage Iv ◽  
Retrieval Algorithm ◽  
Maximum Rainfall ◽  
Tropical Rainfall ◽  
Hourly Rainfall ◽  
Potential Trap ◽  
Eta Model ◽  
Rainfall Estimates

Abstract Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm or greater strength (>65 km h−1). These estimates, known as the tropical rainfall potential (TRaP), are generated in an objective manner by taking instantaneous rainfall estimates from passive microwave sensors, advecting this rainfall pattern along the predicted storm track, and accumulating rainfall over the next 24 h. In this study, the TRaPs generated by SSD during the 2002 Atlantic hurricane season have been validated using National Centers for Environmental Prediction (NCEP) stage IV hourly rainfall estimates. An objective validation package was used to generate common statistics such as correlation, bias, root-mean-square error, etc. It was found that by changing the minimum rain-rate threshold, the results could be drastically different. It was determined that a minimum threshold of 25.4 mm day−1 was appropriate for use with TRaP. By stratifying the data by different criteria, it was discovered that the TRaPs generated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, with its optimal set of measurement frequencies, improved spatial resolution, and advanced retrieval algorithm, produced the best results. In addition, the best results were found for TRaPs generated for storms that were between 12 and 18 h from landfall. Since the TRaP is highly dependent on the forecast track of the storm, selected TRaPs were rerun using the observed track contained in the NOAA/Tropical Prediction Center (TPC) “best track.” Although some TRaPs were not significantly improved by using this best track, significant improvements were realized in some instances. Finally, as a benchmark for the usefulness of TRaP, comparisons were made to Eta Model 24-h precipitation forecasts as well as three climatological maximum rainfall methods. It was apparent that the satellite-based TRaP outperforms the Eta Model in virtually every statistical category, while the climatological methods produced maximum rainfall totals closer to the stage IV maximum amounts when compared with TRaP, although these methods are for storm totals while TRaP is for a 24-h period.

Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

2013 ◽  
Vol 52 (12) ◽  
pp. 2809-2827 ◽  
Author(s):  
Joseph P. Zagrodnik ◽  
Haiyan Jiang
Keyword(s):  
Tropical Cyclones ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Light Rain ◽  
Hourly Rainfall ◽  
Wide Range ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates ◽  
Rainfall Estimates

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

scholarly journals A case study for cyclone ‘Aila’ for forecasting rainfall using satellite derived rain rate data

MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 77-82
Author(s):  
HABIBURRAHAMAN BISWAS ◽  
P.K. KUNDU ◽  
D. PRADHAN
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Storm Track ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Cyclone Aila ◽  
Potential Trap ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates

caxky dh [kkM+h esa cuus ,oa tehu ls Vdjkus okys pØokrh; rwQkuksa ds  ifj.kkeLo:i  Hkkjh o"kkZ dh otg ls if’pe caxky ds rV lesr Hkkjr ds iwohZ rV ds yksxksa dh tku eky dks dkQh [krjk jgrk gSA tehu ls Vdjkus okys m".kdfVca/kh; pØokrh rwQkuksa dh otg ls gksus okyh o"kkZ dh ek=k dk iwokZuqeku djuk cgqr dfBu gSA m".kdfVca/kh; pØokrh; rwQkuksa ds nk;js esa vkus okys o"kkZ okys {ks=ksa esa laHkkfor pØokrh; rwQku ls gksus okys o"kkZ lap;u dk iwokZuqeku djus ds fy, mixzg ls izkIr o"kkZ njksa dk mi;ksx fd;k tk ldrk gSA bl 'kks/k i= esa ‘vkbyk’ ds m".kdfVca/kh; o"kkZ ekiu fe’ku ¼Vh- vkj- ,e- ,e-½] mixzg o"kkZ nj vk¡dM+ksa rFkk rwQku ds ns[ks x, ekxZ dk mi;ksx djrs gq, m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ls 24 ?kVsa igys rVh; LVs’kuksa ij o"kkZ dk vkdyu djus dk iz;kl fd;k x;k gSA la;qDr jkT; vesfjdk esa fodflr lqifjfpr rduhd ds vk/kkj ij  m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ds 24 ?kaVs igys m".kdfVca/kh; o"kkZ foHko ¼Vh- vkj- ,- ih-½ iwokZuqeku fo’ks"k :i  ls rwQku dh fn’kk ds lkeus vkus okys rVh; {ks=ksa ds fy, vPNh o"kkZ dk iwokZuqeku miyC/k djkrk gSA Major threat to the life and property of people on the east coast of India, including West Bengal Coast, is due to very heavy rainfall from landfalling tropical cyclones originated over Bay of Bengal. Forecasting magnitude of rainfall from landfalling tropical cyclones is very difficult. Satellite derived rain rates over the raining areas of tropical cyclones can be used to forecast potential tropical cyclone rainfall accumulations. In the present study, an attempt has been made to estimate 24 hours rainfall over coastal stations before landfall of tropical Cyclone ‘Aila’ using Tropical Rainfall Measuring Mission (TRMM) satellite rain rates data and observed storm track of Aila. Forecast Tropical Rainfall Potential (TRaP), 24 hours prior to landfall for the tropical cyclone ‘Aila’ based on well known technique developed in USA, provides a good rainfall forecast especially for the coastal areas lying at the head of direction of the storm.

scholarly journals Susceptibility mapping and estimation of rainfall threshold using space based input for assessment of landslide hazard in Guwahati city in North East India

2014 ◽  
Vol XL-8 ◽  
pp. 15-19 ◽  
Author(s):  
K. Bhusan ◽  
S. S. Kundu ◽  
K. Goswami ◽  
S. Sudhakar
Keyword(s):  
High Resolution ◽  
Rainfall Threshold ◽  
Landslide Hazard ◽  
Capital City ◽  
Landslide Risk ◽  
Eastern Region ◽  
North East ◽  
Hourly Rainfall ◽  
Guwahati City ◽  
Rainfall Estimates

Slopes are the most common landforms in North Eastern Region (NER) of India and because of its relatively immature topography, active tectonics, and intense rainfall activities; the region is susceptible to landslide incidences. The scenario is further aggravated due to unscientific human activities leading to destabilization of slopes. Guwahati, the capital city of Assam also experiences similar hazardous situation especially during monsoon season thus demanding a systematic study towards landslide risk reduction. A systematic assessment of landslide hazard requires understanding of two components, "where" and "when" that landslides may occur. Presently no such system exists for Guwahati city due to lack of landslide inventory data, high resolution thematic maps, DEM, sparse rain gauge network, etc. The present study elucidates the potential of space-based inputs in addressing the problem in absence of field-based observing networks. First, Landslide susceptibility map in 1 : 10,000 scale was derived by integrating geospatial datasets interpreted from high resolution satellite data. Secondly, the rainfall threshold for dynamic triggering of landslide was estimated using rainfall estimates from Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis. The 3B41RT data for 1 hourly rainfall estimates were used to make Intensity-Duration plot. Critical rainfall was estimated for every incidence by analysing cumulative rainfall leading to a landslide for total of 19 incidences and an empirical rainfall intensity-duration threshold for triggering shallow debris slides was developed (Intensity = 5.9 Duration-0.479).

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

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.

scholarly journals A Combined Multisensor Optimal Estimation Retrieval Algorithm for Oceanic Warm Rain Clouds

2009 ◽  
Vol 48 (11) ◽  
pp. 2242-2256 ◽  
Author(s):  
Anita D. Rapp ◽  
G. Elsaesser ◽  
C. Kummerow
Keyword(s):  
Deep Convection ◽  
Optimal Estimation ◽  
Hydrologic Cycle ◽  
Retrieval Algorithm ◽  
Climate Change Scenarios ◽  
Temperature Changes ◽  
Tropical Rainfall ◽  
Cloud Properties ◽  
Warm Rain ◽  
Rain Cloud

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.

scholarly journals Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

2019 ◽  
Vol 23 (1) ◽  
pp. 207-224 ◽  
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Tirthankar Roy ◽  
Graham P. Weedon ◽  
Florian Pappenberger ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Weather Forecasting ◽  
System Modeling ◽  
Stage Iv ◽  
Radar Data ◽  
Fourth Generation ◽  
Retrieval Algorithm ◽  
Secondary Importance ◽  
Bias Score

Abstract. New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

scholarly journals Ensemble Tropical Rainfall Potential (eTRaP) Forecasts

2011 ◽  
Vol 26 (2) ◽  
pp. 213-224 ◽  
Author(s):  
Elizabeth E. Ebert ◽  
Michael Turk ◽  
Sheldon J. Kusselson ◽  
Jianbin Yang ◽  
Matthew Seybold ◽  
...  
Keyword(s):  
Mean Field ◽  
Heavy Rain ◽  
The United States ◽  
Tropical Rainfall ◽  
Probabilistic Forecasts ◽  
Potential Trap ◽  
Single Sensor ◽  
Atlantic Hurricanes ◽  
Novel Concept ◽  
Rain Rates

Abstract Ensemble tropical rainfall potential (eTRaP) has been developed to improve short-range forecasts of heavy rainfall in tropical cyclones. Evolving from the tropical rainfall potential (TRaP), a 24-h rain forecast based on estimated rain rates from microwave sensors aboard polar-orbiting satellites, eTRaP combines all single-pass TRaPs generated within ±3 h of 0000, 0600, 1200, and 1800 UTC to form a simple ensemble. This approach addresses uncertainties in satellite-derived rain rates and spatial rain structures by using estimates from different sensors observing the cyclone at different times. Quantitative precipitation forecasts (QPFs) are produced from the ensemble mean field using a probability matching approach to recalibrate the rain-rate distribution against the ensemble members (e.g., input TRaP forecasts) themselves. ETRaPs also provide probabilistic forecasts of heavy rain, which are potentially of enormous benefit to decision makers. Verification of eTRaP forecasts for 16 Atlantic hurricanes making landfall in the United States between 2004 and 2008 shows that the eTRaP rain amounts are more accurate than single-sensor TRaPs. The probabilistic forecasts have useful skill, but the probabilities should be interpreted within a spatial context. A novel concept of a “radius of uncertainty” compensates for the influence of location error in the probability forecasts. The eTRaPs are produced in near–real time for all named tropical storms and cyclones around the globe. They can be viewed online (http://www.ssd.noaa.gov/PS/TROP/etrap.html) and are available in digital form to users.

scholarly journals Temporal Downscaling of TRMM Rain-Rate Images Using Principal Component Analysis during Heavy Tropical Thunderstorm Seasons

2015 ◽  
Vol 16 (5) ◽  
pp. 2264-2275 ◽  
Author(s):  
M. Rizaludin Mahmud ◽  
Hiroshi Matsuyama ◽  
Tetsuro Hosaka ◽  
Shinya Numata ◽  
Mazlan Hashim
Keyword(s):  
Principal Component Analysis ◽  
Satellite Data ◽  
Rain Rate ◽  
Daily Rainfall ◽  
Principal Component ◽  
Component Analysis ◽  
Eastern Coast ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Rainfall Estimates

Abstract This paper examines the utility of principal component analysis (PCA) in obtaining accurate daily rainfall estimates from 3-hourly Tropical Rainfall Measuring Mission (TRMM) satellite data during heavy precipitation in a humid tropical environment. A large bias during heavy thunderstorms in humid tropical catchments is indicated by the TRMM satellite and is of profound concern because it is a conspicuous constraint for practical hydrology applications and requires proper treatment, particularly in areas with sparse rain gauges. The common procedure of calculating daily rainfall estimates by direct accumulation (DA) of a series of 3-hourly rainfall estimates caused a large bias because of temporal uncertainties, upscaling effects, and different mechanisms. In this study, PCA was used to transform correlated 3-hourly rain-rate images into a minimum effective principal component and to compute the corresponding rain-rate proportion based on correlation strength. This study was conducted on 91 rainy days of various intensity, acquired from three different years, during the wettest season on the eastern coast of peninsular Malaysia. Results showed that PCA reduced the bias and daily root-mean-square error by an average of 62% and 22%, respectively, compared with the DA approach. The PCA transformation was able to produce more precise daily rainfall estimates compared to the DA approach without the use of any rain gauge references. However, the performance was varied by the threshold selection and rainfall intensity. The results of this study indicate that PCA can be a useful tool in effective temporal downscaling of TRMM satellite data during heavy thunderstorm seasons in areas where rain gauges are sparse and satellite data are pivotal as a secondary source of rainfall data.

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