scholarly journals INTERCOMPARISON OF ANNUAL RAINFALL ESTIMATES OF TARAI REGION USING STATISTICAL DISTRIBUTIONS

MAUSAM ◽  
2021 ◽  
Vol 60 (3) ◽  
pp. 371-378
Author(s):  
N. VIVEKANANDAN
Keyword(s):  
Annual Rainfall ◽  
Statistical Distributions ◽  
Rainfall Estimates

scholarly journals CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use

2015 ◽  
Vol 8 (1) ◽  
pp. 83-112 ◽  
Author(s):  
V. D. J. Keller ◽  
M. Tanguy ◽  
I. Prosdocimi ◽  
J. A. Terry ◽  
O. Hitt ◽  
...  
Keyword(s):  
Monthly Rainfall ◽  
Annual Rainfall ◽  
National Database ◽  
Maximum Information ◽  
Weather Observations ◽  
The Republic ◽  
Areal Rainfall ◽  
The Uk ◽  
Rainfall Estimates

Abstract. The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The dataset was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of raingauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) read raingauges were used in order to obtain maximum information from the raingauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR dataset was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR dataset contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational raingauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty. CEH-GEAR is available free of charge for commercial and non-commercial use subject to licensing terms and conditions. doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e

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 A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan

2020 ◽  
Vol 12 (3) ◽  
pp. 580
Author(s):  
Muhammad Usman ◽  
Janet E. Nichol
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Variability ◽  
Vegetation Indices ◽  
Biomass Productivity ◽  
Temporal Analysis ◽  
Annual Rainfall ◽  
Spatial Estimates ◽  
Spatio Temporal ◽  
Rainfall Estimates

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.

scholarly journals Statistical Study of Rainfall Control: The Dagum Distribution and Applicability to the Southwest of Spain

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 453 ◽  
Author(s):  
Fernando López-Rodríguez ◽  
Justo García-Sanz-Calcedo ◽  
Francisco Moral-García ◽  
Antonio García-Conde
Keyword(s):  
Goodness Of Fit ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Statistical Distributions ◽  
Statistical Tool ◽  
Goodness Of Fit Tests ◽  
Dagum Distribution ◽  
Pearson Type ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

It is of vital importance in statistical distributions to fit rainfall data to determine the maximum amount of rainfall expected for a specific hydraulic work. Otherwise, the hydraulic capacity study could be erroneous, with the tragic consequences that this would entail. This study aims to present the Dagum distribution as a new statistical tool to calculate rainfall in front of frequent statistical distributions such as Gumbel, Log-Pearson Type III, Gen Extreme Value (GEV) and SQRT-ET max. The study was performed by collecting annual rainfall data from 52 meteorological stations in the province of Badajoz (Spain), using the statistical goodness-of-fit tests of Anderson–Darling and Kolmogorov–Smirnov to establish the degree of fitness of the Dagum distribution, applied to the maximum annual rainfall series. The results show that this distribution obtained a flow 21.92% greater than that with the traditional distributions. Therefore, in the Southwest of Spain, the Dagum distribution fits better to the observed rainfall data than other common statistical distributions, with respect to precision and calculus of hydraulics works and river flood plains.

scholarly journals A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Vol 25 (7) ◽  
pp. 4061-4080
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

scholarly journals A climatological benchmark for operational radar rainfall bias reduction

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
Real Time ◽  
Mean Field ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Annual Rainfall ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Derivation Method ◽  
Rainfall Estimates

Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPE) limits its use in hydro-meteorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5-min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability, and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for twelve Dutch catchments and polders. We validated the results against the operational mean field bias (MFB) adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 31 automatic and 325 manual rain gauges. Only the automatic gauges of this network are available in real-time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher far from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. Annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB adjusted rainfall estimates for catchments far from the radars. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

scholarly journals Validation of Satellite Rainfall Estimates over Equatorial East Africa

2021 ◽  
Keyword(s):  
East Africa ◽  
Prediction Models ◽  
Extreme Event ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Annual Rainfall ◽  
Extreme Rainfall Events ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. 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, IMERG, TMPA, CHIRPS and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual timescales, for extreme daily precipitation, and for TMPA and IMERG near real-time (NRT) products. Results show that: 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual timesteps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights; improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.

scholarly journals CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications

2015 ◽  
Vol 7 (1) ◽  
pp. 143-155 ◽  
Author(s):  
V. D. J. Keller ◽  
M. Tanguy ◽  
I. Prosdocimi ◽  
J. A. Terry ◽  
O. Hitt ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
National Database ◽  
Data Set ◽  
Maximum Information ◽  
The Republic ◽  
Areal Rainfall ◽  
The Uk ◽  
Rainfall Estimates

Abstract. The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km2 of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling. The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution. The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890–2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty. CEH-GEAR is available from doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e and is free of charge for commercial and non-commercial use subject to licensing terms and conditions.

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