Spatial interpolation of daily rainfall stochastic generation parameters over East Africa

2014 ◽  
Vol 59 (1) ◽  
pp. 39-60 ◽  
Author(s):  
P Camberlin ◽  
W Gitau ◽  
P Oettli ◽  
L Ogallo ◽  
B Bois
Keyword(s):  
East Africa ◽  
Spatial Interpolation ◽  
Daily Rainfall ◽  
Stochastic Generation

scholarly journals Stochastic Generation of Daily Rainfall, in Kavala City Station, North-Eastern Greece

2016 ◽  
Vol 162 ◽  
pp. 162-171 ◽  
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis ◽  
Sotirios Papadopoulos ◽  
Athanasios Pantrakis
Keyword(s):  
Daily Rainfall ◽  
Stochastic Generation ◽  
North Eastern

scholarly journals Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

2011 ◽  
Vol 15 (7) ◽  
pp. 2259-2274 ◽  
Author(s):  
S. Ly ◽  
C. Charles ◽  
A. Degré
Keyword(s):  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Gaussian Model ◽  
Daily Rainfall ◽  
Spherical Model ◽  
Daily Basis ◽  
Catchment Scale ◽  
Multivariate Geostatistics ◽  
Variogram Models ◽  
Thiessen Polygon

Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.

scholarly journals Spatial interpolation of daily rainfall at catchment scale: a case study of the Ourthe and Ambleve catchments, Belgium

2010 ◽  
Vol 7 (5) ◽  
pp. 7383-7416 ◽  
Author(s):  
S. Ly ◽  
C. Charles ◽  
A. Degré
Keyword(s):  
Spatial Interpolation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Daily Rainfall ◽  
Daily Basis ◽  
Catchment Scale ◽  
Geostatistical Methods ◽  
Weighted Least Squares Method ◽  
Thiessen Polygon ◽  
Single Moment

Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.

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 Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions

2012 ◽  
Vol 464-465 ◽  
pp. 388-400 ◽  
Author(s):  
Paul D. Wagner ◽  
Peter Fiener ◽  
Florian Wilken ◽  
Shamita Kumar ◽  
Karl Schneider
Keyword(s):  
Spatial Interpolation ◽  
Daily Rainfall

scholarly journals Downscaling of Seasonal Rainfall over the Philippines: Dynamical versus Statistical Approaches

2012 ◽  
Vol 140 (4) ◽  
pp. 1204-1218 ◽  
Author(s):  
Andrew W. Robertson ◽  
Jian-Hua Qian ◽  
Michael K. Tippett ◽  
Vincent Moron ◽  
Anthony Lucero
Keyword(s):  
General Circulation ◽  
Spatial Interpolation ◽  
Climate Model ◽  
Daily Rainfall ◽  
Circulation Model ◽  
The Philippines ◽  
Seasonal Rainfall ◽  
Strong Impact ◽  
Wind Fields ◽  
Skill Scores

The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.

A nested multisite daily rainfall stochastic generation model

2009 ◽  
Vol 371 (1-4) ◽  
pp. 142-153 ◽  
Author(s):  
Ratnasingham (Sri) Srikanthan ◽  
Geoffrey G.S. Pegram
Keyword(s):  
Daily Rainfall ◽  
Generation Model ◽  
Stochastic Generation
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