scholarly journals Evaluation of satellite rainfall estimates over Ethiopian river basins

2010 ◽  
Vol 7 (5) ◽  
pp. 7669-7694 ◽  
Author(s):  
T. G. Romilly ◽  
M. Gebremichael
Keyword(s):  
High Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
River Basins ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Annual Total ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Rainfall Estimates

Abstract. The objective of this study was to evaluate the accuracy of high resolution satellite-based rainfall estimates (SREs) across six river basins within Ethiopia during the major (Kiremt) and minor (Belg) rainy seasons for the years 2003 to 2007. The six regions, the Awash, Baro Akobo, Blue Nile, Genale Dawa, Rift Valley and Wabi Shebele River Basins surround the Ethiopian Highlands, which produces different topographical features, as well as spatial and temporal rainfall patterns. Precipitation estimates for the six regions were taken from three widely used high resolution SREs: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. All three SREs show the natural northwest-southeast precipitation gradient, but exhibit different spatial (mean annual total and number of rainy days) and temporal (monthly) totals. When compared to ground based rain gauges throughout the six regions, and for the years of interest, the performance of the three SREs were found to be season independent. The results varied for lower elevations, with CMORPH and TMPA 3B42RT performing better than PERSIANN in the southeast, while PERSIANN provided more accurate results in the northwest. At higher elevations, PERSIANN consistently underestimated while the performance of CMORPH and TMPA 3B42RT varied.

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

2009 ◽  
Vol 10 (2) ◽  
pp. 533-543 ◽  
Author(s):  
Daniel A. Vila ◽  
Luis Gustavo G. de Goncalves ◽  
David L. Toll ◽  
Jose Roberto Rozante
Keyword(s):  
High Resolution ◽  
South America ◽  
Real Time ◽  
Bias Correction ◽  
Control Algorithm ◽  
Daily Precipitation ◽  
Tropical Rainfall Measuring Mission ◽  
Time Algorithm ◽  
Rain Gauges ◽  
Precipitation Analysis

Abstract This paper describes a comprehensive assessment of a new high-resolution, gauge–satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge- and satellite-based analyses of daily precipitation over land in regional domains.

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

2010 ◽  
Vol 49 (5) ◽  
pp. 1044-1051 ◽  
Author(s):  
Feyera A. Hirpa ◽  
Mekonnen Gebremichael ◽  
Thomas Hopson
Keyword(s):  
Neural Networks ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Microwave Data

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

scholarly journals Evaluation of satellite rainfall estimates over Ethiopian river basins

2011 ◽  
Vol 15 (5) ◽  
pp. 1505-1514 ◽  
Author(s):  
T. G. Romilly ◽  
M. Gebremichael
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
River Basins ◽  
Rain Gauge ◽  
Rainfall Regime ◽  
Humid Climate ◽  
Precipitation Estimation ◽  
Satellite Rainfall ◽  
Almost All ◽  
High Elevations ◽  
Rainfall Estimates

Abstract. High resolution satellite-based rainfall estimates (SREs) have enormous potential for use in hydrological applications, particularly in the developing world as an alternative to conventional rain gauges which are typically sparse. In this study, three SREs have been evaluated against collocated rain gauge measurements in Ethiopia across six river basins that represent different rainfall regimes and topography. The comparison is made using five-year (2003–2007) averages, and results are stratified by river basin, elevation and season. The SREs considered are: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. Overall, the microwave-based products TMPA 3B42RT and CMORPH outperform the infrared-based product PERSIANN: PERSIANN tends to underestimate rainfall by 43 %, while CMORPH tends to underestimate by 11 % and TMPA 3B42RT tends to overestimate by 5 %. The bias in the satellite rainfall estimates depends on the rainfall regime, and, in some regimes, the elevation. In the northwest region, which is characterized mainly by highland topography, a humid climate and a strong Intertropical Convergence Zone (ITCZ) effect, elevation has a strong influence on the accuracy of the SREs: TMPA 3B42RT and CMORPH tend to overestimate at low elevations but give reasonably accurate results at high elevations, whereas PERSIANN gives reasonably accurate values at low elevations but underestimates at high elevations. In the southeast region, which is characterized mainly by lowland topography, a semi-arid climate and southerly winds, elevation does not have a significant influence on the accuracy of the SREs, and all the SREs underestimate rainfall across almost all elevations.

scholarly journals Bias correction of daily satellite-based rainfall estimates for hydrologic forecasting in the Upper Zambezi, Africa

2016 ◽  
Author(s):  
Rodrigo Valdés-Pineda ◽  
Eleonora M. C. Demaría ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevilla
Keyword(s):  
Real Time ◽  
Bias Correction ◽  
Principal Component ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Streamflow Forecasts ◽  
Semi Arid Region ◽  
Rainfall Estimates ◽  
Zambezi River

Abstract. The Zambezi Basin is located in the semi-arid region of southern Africa and is one of the largest basins in Africa. The Upper Zambezi River Basin (UZRB) is sparsely gauged (only 11 rain gauges are currently accessible), and real-time rainfall estimates are not readily available. However, Satellite Precipitation Products (SPPs) may complement that information, thereby allowing for improved real-time forecasting of streamflows. In this study, three SPPs for the UZRB are bias-corrected and evaluated for use in real-time forecasting of daily streamflows: (1) CMORPH (Climate Prediction Center’s morphing technique), (2) PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and (3) TRMM-3B42RT (Tropical Rainfall Measuring Mission). Two approaches for bias correction (Quantile Mapping and a Principal Component-based technique) are used to perform Bias Correction (BC) for the daily SPPs; for reference data, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) was used. The two BC approaches were evaluated for the period 2001–2016. The bias-corrected SPPs were then used for real-time forecasting of streamflows at Katima Mulilo in the UZRB. Both BC approaches significantly improve the accuracy of the streamflow forecasts in the UZRB.

scholarly journals Real-Time Bias Reduction for Satellite-Based Precipitation Estimates

2010 ◽  
Vol 11 (6) ◽  
pp. 1275-1285 ◽  
Author(s):  
Yudong Tian ◽  
Christa D. Peters-Lidard ◽  
John B. Eylander
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Bias Reduction ◽  
Training Period ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Precipitation Analysis ◽  
Rain Rates ◽  
Climate Prediction Center

Abstract A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

scholarly journals Reliability of Gridded Precipitation Products in the Yellow River Basin, China

2020 ◽  
Vol 12 (3) ◽  
pp. 374 ◽  
Author(s):  
Yanfen Yang ◽  
Jing Wu ◽  
Lei Bai ◽  
Bing Wang
Keyword(s):  
River Basin ◽  
Yellow River ◽  
Tropical Rainfall Measuring Mission ◽  
Yellow River Basin ◽  
Superior Performance ◽  
The Yellow River ◽  
Basin Scale ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
The Yellow River Basin

Gridded precipitation products are the potential alternatives in hydrological studies, and the evaluation of their accuracy and potential use is very important for reliable simulations. The objective of this study was to investigate the applicability of gridded precipitation products in the Yellow River Basin of China. Five gridded precipitation products, i.e., Multi-Source Weighted-Ensemble Precipitation (MSWEP), CPC Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), were evaluated against observations made during 2001−2014 at daily, monthly, and annual scales. The results showed that MSWEP had a higher correlation and lower percent bias and root mean square error, while CMORPH and GSMaP made overestimations compared to the observations. All the datasets underestimated the frequency of dry days, and overestimated the frequency and the intensity of wet days (0–5 mm/day). MSWEP and TRMM showed consistent interannual variations and spatial patterns while CMORPH and GSMaP had larger discrepancies with the observations. At the sub-basin scale, all the datasets performed poorly in the Beiluo River and Qingjian River, whereas they were applicable in other sub-basins. Based on its superior performance, MSWEP was identified as more suitable for hydrological applications.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

2011 ◽  
Vol 12 (6) ◽  
pp. 1414-1431 ◽  
Author(s):  
David Kitzmiller ◽  
Suzanne Van Cooten ◽  
Feng Ding ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
...  
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Freezing Level ◽  
Distributed Hydrologic Model ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

scholarly journals Validation of Satellite-Based High-Resolution Rainfall Products over the Korean Peninsula Using Data from a Dense Rain Gauge Network

2010 ◽  
Vol 49 (4) ◽  
pp. 701-714 ◽  
Author(s):  
B. J. Sohn ◽  
Hyo-Jin Han ◽  
Eun-Kyoung Seo
Keyword(s):  
High Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Diurnal Cycles ◽  
South Korean ◽  
Precipitation Estimation ◽  
Surface Measurements ◽  
Rain Events ◽  
Using Data ◽  
June July

Abstract Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.

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