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 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 The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall

2013 ◽  
Vol 26 (5) ◽  
pp. 1654-1668 ◽  
Author(s):  
R. C. Blamey ◽  
C. J. C. Reason
Keyword(s):  
South Africa ◽  
Southern Africa ◽  
Rainfall Variability ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Summer Rainfall ◽  
Spatial And Temporal Variability ◽  
Eastern Region ◽  
Southwest Indian Ocean ◽  
Mesoscale Convective

Abstract A combination of numerous factors, including geographic position, regional orography, and local sea surface temperatures, means that subtropical southern Africa experiences considerable spatial and temporal variability in rainfall and is prone to both frequent flooding and drought events. One system that may contribute to rainfall variability in the region is the mesoscale convective complex (MCC). In this study, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data is used to document the precipitation produced by MCCs over southern Africa for the 1998–2006 period. Most of the rainfall associated with MCCs is found to occur over central Mozambique, extending southward to eastern South Africa. High precipitation totals associated with these systems also occur over the neighboring southwest Indian Ocean, particularly off the northeast coast of South Africa. MCCs are found to contribute up to 20% of the total summer rainfall (November–March) in parts of the eastern region of southern Africa. If the month of March is excluded from the analysis, then the contribution increases up to 24%. In general, the MCC summer rainfall contribution for most of the eastern region is approximately between 8% and 16%. Over the western interior and Botswana and Namibia, the MCC contribution is much less (<6%). It is also evident that there is considerable interannual variability associated with the contribution that these systems make to the total warm season rainfall.

scholarly journals TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

scholarly journals Error Analysis of Satellite Precipitation Products in Mountainous Basins

2014 ◽  
Vol 15 (5) ◽  
pp. 1778-1793 ◽  
Author(s):  
Yiwen Mei ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos ◽  
Marco Borga
Keyword(s):  
Error Analysis ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

scholarly journals Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

2016 ◽  
Vol 17 (6) ◽  
pp. 1817-1836 ◽  
Author(s):  
Yagmur Derin ◽  
Emmanouil Anagnostou ◽  
Alexis Berne ◽  
Marco Borga ◽  
Brice Boudevillain ◽  
...  
Keyword(s):  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Global Scale ◽  
Rain Gauge ◽  
Swiss Alps ◽  
Wet Season ◽  
Reanalysis Dataset ◽  
Peruvian Andes ◽  
Mountainous Regions ◽  
Extensive Evaluation

Abstract An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

scholarly journals Evaluating TMPA Rainfall over the Sparsely Gauged East African Rift

2018 ◽  
Vol 19 (9) ◽  
pp. 1507-1528 ◽  
Author(s):  
Elise Monsieurs ◽  
Dalia Bach Kirschbaum ◽  
Jackson Tan ◽  
Jean-Claude Maki Mateso ◽  
Liesbet Jacobs ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
East African Rift ◽  
East African ◽  
High Rainfall ◽  
Two Factors ◽  
African Rift ◽  
Rainfall Estimates

Abstract Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability.

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 Indicator-Based Assessment of Drought in Bhilwara District, Rajasthan

2021 ◽  
pp. 141-153
Author(s):  
Gaurav Sharma ◽  
Chandra Kishor Kumar ◽  
Rishi Pathak
Keyword(s):  
Indian Monsoon ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Drought Frequency ◽  
Arid Conditions ◽  
Dry Spells ◽  
Water Scarce ◽  
Rainfall Departure

Management of water resources helps to sustain even in drastic conditions resulted due to unprecedented disruption in rainfall patterns. Change and irregularity in the pattern of the Indian monsoon are the outcomes of human-induced activities. Inadequate water availability affecting various sectors namely domestic, industrial, and agricultural sectors that are dependent upon it. To cope under such drastic conditions adaptability and planning prior to its occurrence plays a significant role. It is under this context, the present study investigated drought characteristics which include drought frequency and severity caused by prolonged dry spells in Bhilwara district, Rajasthan. The district falls in the water-scarce regions with arid to semi-arid conditions and with an average annual rainfall of 658.03 mm. Due to high rainfall variability, the region is frequently subsumed under drought-like conditions. Comprehensive analysis using daily rainfall data from 1973-2018 for 12 rain gauge stations in Bhilwara district has been carried out. Most of the stations were drought-prone assessed by probability analysis using Weibull’s plotting position formula. The departure analysis showed that Kotri station has a maximum drought frequency of 1 in 2 years while other stations were having a drought frequency of 1 in 3 to 4 years. Prioritization of drought-prone stations based on rainfall departure analysis helps to initiate an immediate mitigation process based on the ranking of its proneness. Asind and Bhilwara station with RDI 0.91 has maximum proneness and its calls for effective planning for drought management. Based on seasonal departure analysis it is found that 1980, 1981, 1985, 1987, 2000, 2002, 2008, 2015, and 2017 are drought years. The result has been supplemented using NDVI so that the vegetation condition can also be assessed during water stress conditions. The study highlighted that the frequency of drought has increased owing due to climate change and thus, poses serious challenges if not tackle adequately.

scholarly journals Uncertainty Assessments of Satellite Derived Rainfall Products

2016 ◽  
Author(s):  
Margaret Kimani ◽  
Joost Hoedjes ◽  
Zhongbo Su
Keyword(s):  
Large Scale ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Climate Data ◽  
Error Distributions ◽  
Precipitation Estimation ◽  
Scatter Plots ◽  
Global Precipitation

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

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.

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