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.

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

scholarly journals A New Parameter to Assess Impact of Rain Gauge Density on Uncertainty in the Estimate of Monthly Rainfall over India

2019 ◽  
Vol 20 (5) ◽  
pp. 821-832 ◽  
Author(s):  
Satya Prakash ◽  
Ashwin Seshadri ◽  
J. Srinivasan ◽  
D. S. Pai
Keyword(s):  
Theoretical Model ◽  
Central India ◽  
Ground Truth ◽  
Accurate Method ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Spatial Density ◽  
Spatial Average ◽  
Rain Gauges ◽  
Rainfall Estimates

Abstract Rain gauges are considered the most accurate method to estimate rainfall and are used as the “ground truth” for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the “averaging error factor” has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty.

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>

Spatio-temporal Trend Detection of Rainfall for Climate Change Assessment in Ahmedabad-Gandhinagar District of Gujarat State, India

2021 ◽  
Vol 7 (1) ◽  
pp. 69-78
Author(s):  
Geeta S. Joshi ◽  
Payal Makhasana
Keyword(s):  
Climate Change ◽  
Rain Gauge ◽  
The State ◽  
Monthly Rainfall ◽  
Trend Detection ◽  
Annual Rainfall ◽  
Sen’S Slope Estimator ◽  
Sen’S Slope ◽  
Mk Test ◽  
Slope Estimator

The present research aims to assess the historical change in rainfall patterns with the changing climate in the Ahmedabad-Gandhinagar district in the state of Gujarat in India. The Mann-Kendall (MK) test along with Sen’s slope estimator have been used for detecting the trend of rainfall data series. The trend of annual rainfall is carried out for – (1) six rain gauge stations established by the State Water Data Center (SWDC) and (2) 11 grid data available from the National Center for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) for 35 years starting from 1979 to 2013. Results obtained from these two data sets for the trend detection were found consistent. Furthermore, the analyses of annual and monthly rainfall using MK test and Sen’s slope estimator at six rain gauge stations are carried out in three time periods i.e. 1974-1987, 1988-2001 and 2002-2016. The inverse distance weighted (IDW) method of interpolation is used for the results obtained from the spatial distribution of the temporal rainfall trend for interpolating the station value over the study area. Annual rainfall for data length of 1979 to 2013 shows an increasing trend. The trend of annual and monthly rainfall for July and September shows a positive trend for the span 2002-2016. This study would be useful to the water resource department and policymakers for climate change adaptation in the study area.

scholarly journals Gridded Monthly Rainfall Estimates Derived from Historical Atoll Observations

2019 ◽  
Vol 36 (4) ◽  
pp. 671-687 ◽  
Author(s):  
Werner E. Cook ◽  
J. Scott Greene
Keyword(s):  
Ordinary Kriging ◽  
Global Precipitation Climatology Project ◽  
Monthly Rainfall ◽  
Standard Errors ◽  
Analysis Tool ◽  
University Of Oklahoma ◽  
Kriging Variance ◽  
The Pacific ◽  
Areal Rainfall ◽  
Rainfall Estimates

AbstractTo provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.

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 Spatial-Temporal Assessment of Satellite-Based Rainfall Estimates in Different Precipitation Regimes in Water-Scarce and Data-Sparse Regions

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 901
Author(s):  
Alaba Boluwade
Keyword(s):  
Temporal Structure ◽  
Extreme Weather ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Precipitation Measurement ◽  
Precipitation Regimes ◽  
Precipitation Analysis ◽  
Rainfall Estimates

Accurate precipitation measurement is very important for socio-hydrological resilience in the face of frequent extreme weather events such as cyclones. This study evaluates the performance of two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA, hereafter: TRMM) and the Integrated Multi-satellite Retrievals for GPM (IMERG, Final Run V06, hereafter: GPM) in the Sultanate of Oman. Oman is an arid country that generally has few rainy days, but has experienced significant flash floods, tropical storms and cyclones recently, leading to the loss of lives and millions of dollars in damage. Accurate precipitation analysis is crucial in flood monitoring, hydrologic modeling, and the estimation of the water balance of any basin, and the lack of a sufficient weather monitoring network is a barrier to accurate precipitation measurement. Satellite rainfall estimates can be a reliable option in sparse network areas, especially in arid and semi-arid countries. This study evaluated monthly rainfall (hereafter: OBSERVED) levels at 77 meteorological stations from January 2016 to December 2018. The capacity of the TRMM and GPM satellite products to detect monthly rainfall amounts at varying precipitation thresholds was also evaluated. Findings included (1) overall and across the 11 Governorates of Oman, both satellite products show different spatial variability and performance to the OBSERVED at the monthly, seasonal, and annual temporal scales; (2) from the perspective of precipitation detection and frequency bias, GPM showed a similar performance to TRMM at detecting low precipitation (2 mm/month) but was poorer at detecting high precipitation (>30 mm/month) across the entire country as well as in the Northern, Interior, and Dhofar regions; (3) both products show similarities to the OBSERVED through the partitioning of their seasonal time series into a distinct number of homogenous segments; and (4) both products had difficulty reproducing OBSERVED levels in the Dhofar and Interior regions, which is consistent with studies conducted in mountainous and coastal regions. With the aim of reproducing the spatial and temporal structure of OBSERVED in a rugged terrain, the study shows that both satellite products can be used in areas of sparse rain gauges or as additional observation for studies of extreme weather events. Overall, this study suggests that for Oman, both satellite products can be used as proxies for OBSERVED with appropriate bias corrections and GPM is also a reliable replacement for TRMM as a precipitation satellite product. The findings will be useful to the country’s flood resilience and mitigation efforts, especially in areas where there is sparse rain gauge coverage.

scholarly journals Probability and Trend Analysis of Monthly Rainfall in Haryana

2019 ◽  
Vol 34 (02) ◽  
Author(s):  
Mohit Nain ◽  
B. K. Hooda
Keyword(s):  
Trend Analysis ◽  
Monsoon Rainfall ◽  
Kendall Test ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Sen’S Slope Estimator ◽  
Sen’S Slope ◽  
Mann Kendall Test ◽  
Slope Estimator

Study on rainfall pattern of a region over a number of years is very useful for crop planning and irrigations scheduling. The present study deals with the probability and trend analysis of monthly rainfall in selected rain gauge stations scattered over the entire state of Haryana. Probabilities for drought, normal and abnormal events for monthly rainfall have been worked out using monthly rainfall data for 42 years (1970-2011), covering 27 rain gauge stations in the state of Haryana. Analysis indicated that drought months are more probable than normal months while normal months are more probable than abnormal months. The monotonic trend direction and magnitude of change in rainfall over time have been examined using the Mann-Kendall test and Sen’s slope estimator tests. Using the Mann-Kendall test and Sen’s slope estimator, the significant decrease in annual rainfall was noticed at Ballabgarh and Thanesar, While in monsoon rainfall, a significant decrease was noticed at Thanesar and Narnaul. But Sirsa is the only district which shows a significant increase in annual and monsoon rainfall. In probability analysis the highest per cent of normal, draughts and abnormal months was observed for Ambala, Hassanpur and Dujana respectively.

Sign in / Sign up

Export Citation Format

Share Document