scholarly journals Validation and Intercomparison of Satellite Rainfall Estimates over Colombia

2010 ◽  
Vol 49 (5) ◽  
pp. 1004-1014 ◽  
Author(s):  
Tufa Dinku ◽  
Franklyn Ruiz ◽  
Stephen J. Connor ◽  
Pietro Ceccato
Keyword(s):  
Complex Terrain ◽  
Pacific Coast ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Poor Performance ◽  
Naval Research ◽  
Mountain Ranges ◽  
The North ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP_MVK and GSMaP_MVK+). The validation and intercomparison of these products is done for the whole as well as different parts of the country. Validation results are reasonably good for daily rainfall over such complex terrain. The best results were obtained for the eastern plain, and the performance of the products was relatively poor over the Pacific coast. In comparing the different satellite products, it was seen that PERSIANN and GSMaP-MVK exhibited poor performance, with significant overestimation by PERSSIAN and serious underestimation by GSMaP-MVK. CMORPH and GSMaP-MVK+ exhibited the best performance among the products evaluated here.

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.

scholarly journals An assessment of the accuracy of global rainfall estimates without ground-based observations

2017 ◽  
Author(s):  
Christian Massari ◽  
Wade Crow ◽  
Luca Brocca
Keyword(s):  
Daily Rainfall ◽  
Global Scale ◽  
Hydrologic Models ◽  
Reanalysis Product ◽  
Eastern Australia ◽  
Wide Range ◽  
America South ◽  
Satellite Rainfall ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. Satellite-based rainfall estimates have great potential value for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of Triple Collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated products of this variable. However, TC requires the simultaneous availability of three products with mutually-uncorrelated errors, a requirement that is difficult to satisfy among current global precipitation datasets. In this study, a recently-developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high quality ground rainfall product over the Contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (versus an unknown truth) of the aforementioned products without using a ground benchmark dataset. The analysis is carried out during the period 2012–2015 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high accuracy of the satellite rainfall estimates in Eastern North and South America, South Africa, Southern and Eastern Asia, Eastern Australia as well as Southern Europe and complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the northern hemisphere and the second providing very good performance in the southern hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely- gauged areas and provide the basis for an optimal integration among different rainfall products.

Validation of Satellite Rainfall Estimates over Equatorial East Africa

2021 ◽  
Author(s):  
Simon Ageet ◽  
Andreas Fink ◽  
Marlon Maranan
Keyword(s):  
East Africa ◽  
Extreme Values ◽  
Extreme Event ◽  
Weather Prediction ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Extreme Rainfall Events ◽  
Satellite Rainfall ◽  
Rainfall Estimates

<p>The sparsity of rain gauge (RG) data over Africa is a known impediment to the assessments of hydro-meteorological risks and of the skill of numerical weather prediction (NWP) models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms.  Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–2018, this study performs a multi-scale evaluation of gauge-calibrated SREs, namely, Integrated Multi-satellite Retrieval for Global Precipitation Measurement (GPM) (IMERG), Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). Skills were assessed from daily to annual timescales, for extreme daily precipitation, and for the TMPA and IMERG near real-time (NRT) products. Results show that: 1) the satellite products reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best overall for shorter temporal scales (daily, pentadal and dekadal) while MSWEP and CHIRPS perform best at the monthly and annual timesteps, respectively; 3) the SREs’ performance, especially in MSWEP, shows high spatial variability likely due to the variation of weights assigned during gauge calibration; 4) all the SREs miss between 57% (IMERG NRT)  and 83 (CHIRPS) of daily extreme rainfall events recorded in the RGs; 5) IMERG NRT outperforms all the other products regarding extreme event detection and accuracy; and 6) for assessing return values of daily extreme values, IMERG and MSWEP are satisfactory while the use of CHIRPS cannot be recommended. The study highlights some improvements of IMERG over its predecessor TMPA and the potential of Multi-Source Weighted-Ensembles products such as MSWEP for flood risk assessment and validation of NWP rainfall forecasts over East Africa.</p>

scholarly journals Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset

2014 ◽  
Vol 15 (5) ◽  
pp. 1810-1831 ◽  
Author(s):  
Helen Greatrex ◽  
David Grimes ◽  
Tim Wheeler
Keyword(s):  
Complex Terrain ◽  
Calibration Procedure ◽  
Calibration Data ◽  
New Developments ◽  
Simulation Methodology ◽  
Satellite Technology ◽  
Calibration Dataset ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

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 Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

2018 ◽  
Vol 378 ◽  
pp. 59-65 ◽  
Author(s):  
Thomas Matingo ◽  
Webster Gumindoga ◽  
Hodson Makurira
Keyword(s):  
Flash Flood ◽  
Daily Rainfall ◽  
Flash Floods ◽  
Rain Gauge ◽  
Hydrological Modelling ◽  
Probability Of Detection ◽  
Time Step ◽  
Flood Modelling ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff) and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs) for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013–2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD) of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC) was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System) daily model calibration Nash Sutcliffe efficiency (NSE) for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015–2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized hydrological processes such as flash floods for sub-daily rainfall, because of improved spatial and temporal resolution.

scholarly journals An assessment of the performance of global rainfall estimates without ground-based observations

2017 ◽  
Vol 21 (9) ◽  
pp. 4347-4361 ◽  
Author(s):  
Christian Massari ◽  
Wade Crow ◽  
Luca Brocca
Keyword(s):  
High Performance ◽  
Daily Rainfall ◽  
Global Scale ◽  
Data Sets ◽  
Data Set ◽  
Reanalysis Product ◽  
Eastern Australia ◽  
Wide Range ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.

scholarly journals An Investigation of Warm-Season Spatial Rainfall Variability in Oklahoma City: Possible Linkages to Urbanization and Prevailing Wind

2009 ◽  
Vol 48 (2) ◽  
pp. 251-269 ◽  
Author(s):  
Lauren M. Hand ◽  
J. Marshall Shepherd
Keyword(s):  
Rainfall Variability ◽  
Warm Season ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Anomaly ◽  
Reanalysis Data ◽  
Oklahoma City ◽  
Prevailing Wind ◽  
Rainfall Estimates

Abstract This study used 9 yr (1998–2006) of warm-season (June–September) mean daily cumulative rainfall data from both the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis and rain gauge stations to examine spatial variability in warm-season rainfall events around Oklahoma City (OKC). It was hypothesized that with warm-season rainfall variability, under weakly forced conditions, a rainfall anomaly would be present in climatological downwind areas of OKC. Results from both satellite and gauge-based analyses revealed that the north-northeastern (NNE) regions of the metropolitan OKC area were statistically wetter than other regions. Climatological sounding and reanalysis data revealed that, on average, the NNE area of OKC was the climatologically downwind region, confirming that precipitation modification by the urban environment may be more dominant than agricultural/topographic influences on weakly forced days. The study also established that satellite precipitation estimates capture spatial rainfall variability as well as traditional ground-based resources do. TRMM products slightly underestimate the precipitation recorded by gauges, but the correlation R improves dramatically when the analysis is restricted to mean daily rainfall estimates from OKC urban grid cells containing multiple gauge stations (R2 = 0.878). It was also quantitatively confirmed, using a relatively new concentration factor analysis, that prevailing wind–rainfall yields were consistent with the overall framework of an urban rainfall effect. Overall, the study establishes a prototype method for utilizing satellite-based rainfall estimates to examine rainfall modification by urbanization on global scales and in parts of the world that are not well instrumented with rain gauge or radar networks.

scholarly journals Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru)

2016 ◽  
Vol 20 (1) ◽  
pp. 125-141 ◽  
Author(s):  
L. Mourre ◽  
T. Condom ◽  
C. Junquas ◽  
T. Lebel ◽  
J. E. Sicart ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Poor Performance ◽  
Atmospheric Model ◽  
Kriging Interpolation ◽  
Annual Rainfall ◽  
Tropical Regions ◽  
Rainfall Patterns

Abstract. The estimation of precipitation over the broad range of scales of interest for climatologists, meteorologists and hydrologists is challenging at high altitudes of tropical regions, where the spatial variability of precipitation is important while in situ measurements remain scarce largely due to operational constraints. Three different types of rainfall products – ground based (kriging interpolation), satellite derived (TRMM3B42), and atmospheric model outputs (WRF – Weather Research and Forecasting) – are compared for 1 hydrological year in order to retrieve rainfall patterns at timescales ranging from sub-daily to annual over a watershed of approximately 10 000 km2 in Peru. An ensemble of three different spatial resolutions is considered for the comparison (27, 9 and 3 km), as long as well as a range of timescales (annual totals, daily rainfall patterns, diurnal cycle). WRF simulations largely overestimate the annual totals, especially at low spatial resolution, while reproducing correctly the diurnal cycle and locating the spots of heavy rainfall more realistically than either the ground-based KED or the Tropical Rainfall Measuring Mission (TRMM) products. The main weakness of kriged products is the production of annual rainfall maxima over the summit rather than on the slopes, mainly due to a lack of in situ data above 3800 m a. s. l.  This study also confirms that one limitation of TRMM is its poor performance over ice-covered areas because ice on the ground behaves in a similar way as rain or ice drops in the atmosphere in terms of scattering the microwave energy. While all three products are able to correctly represent the spatial rainfall patterns at the annual scale, it not surprisingly turns out that none of them meets the challenge of representing both accumulated quantities of precipitation and frequency of occurrence at the short timescales (sub-daily and daily) required for glacio-hydrological studies in this region. It is concluded that new methods should be used to merge various rainfall products so as to make the most of their respective strengths.

Sign in / Sign up

Export Citation Format

Share Document