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 Critical Assessment of Microphysical Assumptions within TRMM Radiometer Rain Profile Algorithm Using Satellite, Aircraft, and Surface Datasets from KWAJEX

2006 ◽  
Vol 45 (5) ◽  
pp. 754-786 ◽  
Author(s):  
Steven T. Fiorino ◽  
Eric A. Smith
Keyword(s):  
Rain Rate ◽  
Cloud Model ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Mixed Phase ◽  
Retrieval Algorithm ◽  
Phase Layer ◽  
High Emission ◽  
Rain Rates

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.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

2005 ◽  
Vol 22 (4) ◽  
pp. 365-380 ◽  
Author(s):  
David B. Wolff ◽  
D. A. Marks ◽  
E. Amitai ◽  
D. S. Silberstein ◽  
B. L. Fisher ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Marshall Islands ◽  
Error Statistics ◽  
Tropical Rainfall ◽  
Nasa Goddard Space ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Rates ◽  
Good Agreement

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent validation techniques show good agreement between gauge-measured and radar-estimated rainfall. A comparison of the NASA GV products and those developed independently by the University of Washington for a subset of data from the Kwajalein Atoll site also shows good agreement. A comparison of NASA GV rain intensities to satellite retrievals from the TRMM Microwave Imager (TMI), precipitation radar (PR), and Combined (COM) algorithms is presented, and it is shown that the GV and satellite estimates agree quite well over the open ocean.

scholarly journals Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones

2016 ◽  
Vol 33 (7) ◽  
pp. 1539-1556 ◽  
Author(s):  
Paula J. Brown ◽  
Christian D. Kummerow ◽  
David L. Randel
Keyword(s):  
Tropical Cyclones ◽  
Vertical Profile ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Initial Assessment ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
One Year ◽  
Trmm Microwave Imager ◽  
Rain Rates

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.

scholarly journals Evaluasi Kesesuaian Data Tropical Rainfall Measuring Mission (TRMM) dengan Data Pos Hujan Pada Das Temef di Kabupaten Timor Tengah Selatan

2020 ◽  
Vol 16 (1) ◽  
pp. 51-62
Author(s):  
Denik Sri Krisnayanti ◽  
Davianto Frangky B. Welkis ◽  
Fery Moun Hepy ◽  
Djoko Legono
Keyword(s):  
Correlation Coefficient ◽  
Correction Factor ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Monthly Basis ◽  
Discharge Data ◽  
Tropical Rainfall ◽  
South Central

The construction of the Temef Dam in Oenino Village, Oenino District, and Konbaki Village, Polen District, South Central Timor Regency requires long and reliable rainfall data. To overcome the minimum data or the unavailability of automatic rainfall (ARR) and discharge data in the past decades, the use of Tropical Rainfall Measuring Mission (TRMM) satellite data is foreseen. The accuracy of TRMM data is obtained when the parameters of suitability and compatibility of TRMM are in a good agreement with the ARR. For the Temef watershed, there are six rainfall stations that were reviewed, namely Fatumnasi, Oeoh, Noelnoni, Polen, Nifukani, and Batinifukoko rainfall stations. Direct comparisons of rainfall data were conducted for 20 years (1998-2018) with temporal resolution on a monthly and daily basis. The results of the study show that the rainfall patterns in the TRMM data product (version 3B42V7) tend to be consistent with 3 rainfall stations in the Temef watershed namely Noelnoni, Fatumnasi, and Batinifukoko. A correlation coefficient of 0.505 – 0.813 was obtained from TRMM data calibration at monthly basis while a correction factor level of 0.0056 - 0.0129 was obtained for daily.  The calibration on the annual maximum daily rainfall data resulted in a correction factor of 0.0298 - 0.2516. Monthly and daily TRMM data fit well with the data of 3 rainfall stations. However, the Noelnoni rainfall station showed poor results on the annual maximum daily rainfall.Keywords: TRMM data, ARR data, correction factor, correlation coefficient

scholarly journals KORELASI ANTARA DATA CURAH HUJAN PENAKAR MANUAL DAN TRMM (TROPICAL RAINFALL MEASURING MISSION) GIOVANNI TOVAS. (STUDI KASUS TEKNOLOGI MODIFIKASI CUACA UNTUK MENANGGULANGI KABUT ASAP KEBAKARAN HUTAN DAN LAHAN DI RIAU TAHUN 2014)

2014 ◽  
Vol 15 (2) ◽  
pp. 51
Author(s):  
Alfan Muttaqin ◽  
Tukiyat Tukiyat ◽  
Purwadi Purwadi ◽  
Tri Handoko Seto
Keyword(s):  
Pearson Correlation ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Stream Line ◽  
Line Pattern ◽  
Tropical Rainfall ◽  
Start Date ◽  
Correlation Data ◽  
Average Rainfall ◽  
Cartesian Diagram

Intisari  Telah dilakukan kajian tentang korelasi antara data curah hujan yang terukur di Pos Meteorologi dan data curah hujan yang terukur pada TRMM (Tropical Rainfall Measuring Mission). Data curah hujan merupakan data curah hujan rata – rata harian dibeberapa titik yang ada di Provinsi Riau. Data yang digunakan dalam kajian ini adalah data hujan dari tanggal 16 Maret sampai dengan 28 April 2014. Pengujian korelasi dilakukan dengan menggunakan teknik korelasi Product Moment Pearson dengan asumsi bahwa data terdistribusi normal. Dari hasil perhitungan dengan korelasi Pearson didapatkan nilai korelasi sebesar 0,52. Korelasi yang terhitung sebesar 0,52 masuk kedalam kategori CUKUP. Selain pengujian korelasi, data curah hujan juga dianalisa dengan uji perbandingan streamline yang terbentuk pada diagram kartesius. Tanpa melihat besarnya nilai curah hujan tetapi dengan melihat pola terbentuknya streamline terlihat tanggal 16 Maret sampai dengan tanggal 21 Maret 2014 pola yang terbentuk cenderung berkebalikan dimana ketika data curah hujan pada Posmet turun curah hujan pada TRMM justru malah naik. Sementara mulai tanggal 22 Maret 2014 sampai akhir tanggal 28 April 2014 terlihat tren stream line yang cenderung berpola mirip walaupun ada beberapa titik yang saling berkebalikan sehingga secara umum terlihat polanya mirip.Abstract  Studied correlation between data rainfall in Post Meteorological (Posmet) and rainfall data on TRMM (Tropical Rainfall Measuring Mission) have been done. Precipitacion  is average rainfall data at the daily average some point in Province of Riau. Data used in this study is  data from 16 March to 28 April, 2014. Correlation testing was done by using Pearson Product Moment correlation with assumption that data were normally distributed. From the calculation of Pearson correlation obtained correlation value is 0.52. Correlations were calculated is 0.52, category ENOUGH. In addition to testing the correlation, data rainfall were also analyzed with a streamlined comparison test were formed on Cartesian diagram. Without seeing the value of rainfall but by looking at the pattern formation of streamlined look dated March 16 until March 21, 2014 established pattern which tends to reverse when the rainfall data Posmet down on TRMM rainfall actually even go up. While the start date of March 22, 2014 until the end date of 28 April 2014 visible trends that tend to stream line pattern is similar, although there are a few points of each other so that in general the pattern looks similiar. 

scholarly journals Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales

2008 ◽  
Vol 47 (8) ◽  
pp. 2215-2237 ◽  
Author(s):  
David B. Wolff ◽  
Brad L. Fisher
Keyword(s):  
Rain Rate ◽  
Spatial Scales ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Global Ocean ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Events ◽  
Trmm Microwave Imager ◽  
Rain Rates

Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.

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.

Sign in / Sign up

Export Citation Format

Share Document