In the context of climate change,countries in West Africa are faced with recurrent flooding with catastrophic consequences,that makes it imperative to have access to rainfall information on fine spatial and temporal s...In the context of climate change,countries in West Africa are faced with recurrent flooding with catastrophic consequences,that makes it imperative to have access to rainfall information on fine spatial and temporal scales for better monitoring and prediction of these phenomena,as could be provided by weather radars.Based on an extensive archive of data from the X-band polarimetric radar and rain gauges observations gathered during the intensive AMMA campaigns in 2006–2007 and the Megha-Tropiques satellite measurement validation programme in 2010 in West Africa,we(i)simulated jointly realistic data for polarimetric radar variables and rain intensity using copula,and(ii)assessed rain rate estimation methods based on neural network(NN)inversion techniques and non-linearly calibrated parametric algorithms.The assessment of rainfall rate retrieval by these estimators is carried out using the part of the observations database not employed for calibration steps.The multiparametric algorithms R(ZH,K_(DP))and R(Z_(DR),K_(DP))perform better than R(ZH,Z_(DR))and R(ZH,Z_(DR),K_(DP)),especially since they are calibrated using copulas with upper tail dependencies,with KGE ranging in 0.68–0.75 and 0.79–0.82,respectively versus ranges of 0.40–0.64 and 0.20–0.51,for the two latter estimators.The neural network-based estimators RNN(Z_(DR),K_(DP))and RNN(ZH,K_(DP)),show KGE score characteristics comparable to those obtained from the best parametric relations,specifically optimized for the synthetic copula-based dataset.However,the neural network-based estimators were shown to be more robust when applied to a specific rainfall event.More specifically,neural network-based estimators trained on synthetic data are sensitive to the copulas’ability to capture the dependence between the variables of interest over the entire distribution of joint values.This leads to a near-cancellation of sensitivity to variability in the raindrop size distribution,as shown the coefficients of correlation near 1,especially for RNN(Z_(DR),K_(DP)),and for less extent RNN(Z_(H),K_(DP)).展开更多
Rain flooding during June on the West Africa coastal area is analyzed by using the 95th and 75th percentiles, which represent extreme and intense rainfall events respectively. Thus, the contribution of these events th...Rain flooding during June on the West Africa coastal area is analyzed by using the 95th and 75th percentiles, which represent extreme and intense rainfall events respectively. Thus, the contribution of these events that reaches around 50% shows their impact on the rainfall in June. Atmospheric and oceanic factors influence the rain flooding. Indeed, the extreme events are associated with easterly waves propagating from 20<span style="white-space:nowrap;">°</span>E, while those of intense events are initiated around 5<span style="white-space:nowrap;">°</span>E. The impact of oceanic conditions exhibits the warming of the equatorial rail and the Atlantic cold tongue, the warming of the whole ocean basin and a north-south dipole of SST anomalies. The West African monsoon that reaches Abidjan corresponds to a low-level atmospheric flow, whose upward motion extends in latitude from the ocean to the continent. An increase of disturbance contributes to enhancing these events. This is confirmed by the inflow on to the continent of oceanic moisture coming from the ventilation by evaporation of warm water. In addition, the coupled ocean-atmosphere simulations are one of the best candidates that could help to better explain these dramatic events. This study is useful because of showing solutions that could help in adoption of policies for the risks management related to these events.展开更多
This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana...This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.展开更多
Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of d...Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of disdrometer (the impact JW RD-69 disdrometer and the Optical Spectro-Pluviometer, OSP) are used to study the consistency of the reflectivity factor-rain rate at the ground (Z-R) relationship variability. The results clearly confirm that the relationship Z-R knows a large spatial variability, from a type of precipitation to another and within the same precipitation regardless the type of disdrometer used for DSD measurements. Base on the similarity of the relations reflectivity factor-rain rate and ratio median volume diameter over the total number of drops-rain rate, the variability of the Z-R coefficients (A, b) through the simultaneously implication of the size and number of drops which characterize the DSD was exhibited. It was shown that the relationships A-α and b-β designed to understand the involvement of parameters D0 and NT of DSD in the variability of the relationship Z-R are similar regardless the types of disdrometer used. However, the relations A-α in the Sahelian region appear to deviate from those of Guinean, equatorial and Soudanian zones. The plausible reasons were discussed.展开更多
文摘In the context of climate change,countries in West Africa are faced with recurrent flooding with catastrophic consequences,that makes it imperative to have access to rainfall information on fine spatial and temporal scales for better monitoring and prediction of these phenomena,as could be provided by weather radars.Based on an extensive archive of data from the X-band polarimetric radar and rain gauges observations gathered during the intensive AMMA campaigns in 2006–2007 and the Megha-Tropiques satellite measurement validation programme in 2010 in West Africa,we(i)simulated jointly realistic data for polarimetric radar variables and rain intensity using copula,and(ii)assessed rain rate estimation methods based on neural network(NN)inversion techniques and non-linearly calibrated parametric algorithms.The assessment of rainfall rate retrieval by these estimators is carried out using the part of the observations database not employed for calibration steps.The multiparametric algorithms R(ZH,K_(DP))and R(Z_(DR),K_(DP))perform better than R(ZH,Z_(DR))and R(ZH,Z_(DR),K_(DP)),especially since they are calibrated using copulas with upper tail dependencies,with KGE ranging in 0.68–0.75 and 0.79–0.82,respectively versus ranges of 0.40–0.64 and 0.20–0.51,for the two latter estimators.The neural network-based estimators RNN(Z_(DR),K_(DP))and RNN(ZH,K_(DP)),show KGE score characteristics comparable to those obtained from the best parametric relations,specifically optimized for the synthetic copula-based dataset.However,the neural network-based estimators were shown to be more robust when applied to a specific rainfall event.More specifically,neural network-based estimators trained on synthetic data are sensitive to the copulas’ability to capture the dependence between the variables of interest over the entire distribution of joint values.This leads to a near-cancellation of sensitivity to variability in the raindrop size distribution,as shown the coefficients of correlation near 1,especially for RNN(Z_(DR),K_(DP)),and for less extent RNN(Z_(H),K_(DP)).
文摘Rain flooding during June on the West Africa coastal area is analyzed by using the 95th and 75th percentiles, which represent extreme and intense rainfall events respectively. Thus, the contribution of these events that reaches around 50% shows their impact on the rainfall in June. Atmospheric and oceanic factors influence the rain flooding. Indeed, the extreme events are associated with easterly waves propagating from 20<span style="white-space:nowrap;">°</span>E, while those of intense events are initiated around 5<span style="white-space:nowrap;">°</span>E. The impact of oceanic conditions exhibits the warming of the equatorial rail and the Atlantic cold tongue, the warming of the whole ocean basin and a north-south dipole of SST anomalies. The West African monsoon that reaches Abidjan corresponds to a low-level atmospheric flow, whose upward motion extends in latitude from the ocean to the continent. An increase of disturbance contributes to enhancing these events. This is confirmed by the inflow on to the continent of oceanic moisture coming from the ventilation by evaporation of warm water. In addition, the coupled ocean-atmosphere simulations are one of the best candidates that could help to better explain these dramatic events. This study is useful because of showing solutions that could help in adoption of policies for the risks management related to these events.
文摘This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain </span><span style="font-family:Verdana;">gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were</span><span style="font-family:Verdana;"> used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> estimator developed </span><span style="font-family:Verdana;">with the contribution of microphysical processes and non-linear least</span></span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal </span><i><span style="font-family:Verdana;">Z</span></i><span style="font-family:Verdana;">-</span><i><span style="font-family:Verdana;">R</span></i><span style="font-family:Verdana;"> algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.
文摘Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of disdrometer (the impact JW RD-69 disdrometer and the Optical Spectro-Pluviometer, OSP) are used to study the consistency of the reflectivity factor-rain rate at the ground (Z-R) relationship variability. The results clearly confirm that the relationship Z-R knows a large spatial variability, from a type of precipitation to another and within the same precipitation regardless the type of disdrometer used for DSD measurements. Base on the similarity of the relations reflectivity factor-rain rate and ratio median volume diameter over the total number of drops-rain rate, the variability of the Z-R coefficients (A, b) through the simultaneously implication of the size and number of drops which characterize the DSD was exhibited. It was shown that the relationships A-α and b-β designed to understand the involvement of parameters D0 and NT of DSD in the variability of the relationship Z-R are similar regardless the types of disdrometer used. However, the relations A-α in the Sahelian region appear to deviate from those of Guinean, equatorial and Soudanian zones. The plausible reasons were discussed.
