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- Author or Editor: Rafael L. Bras x
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Abstract
A numerical mesoscale model has been used to investigate the impact of mesoscale circulations on the distribution of precipitation and cloudiness over a deforested area in Amazonia. Observed patterns of deforestation in Rondônia, Amazonia, with scales on the order of 10 km were used in this study to describe land surface conditions. Various simulations have been performed to identify the conditions under which the mesoscale circulations induced by the heterogeneous land surface could enhance cloudiness and local rainfall. The simulation results suggest that the synoptic forcing, in terms of atmospheric stability and background horizontal wind, dominates during the rainy season; synoptic conditions were so favorable to moist convection that the added effect of surface heterogeneity was negligible. During the dry season, a noticeable impact of mesoscale circulations resulting in enhancement of shallow clouds was simulated; the mesoscale circulations also triggered scattered deep convection that altered the spatial distribution of precipitation. During the break period, the transition from the rainy season to the dry season, the impact of mesoscale circulations on low-level clouds was evident only after reducing the magnitude of the background wind.
Abstract
A numerical mesoscale model has been used to investigate the impact of mesoscale circulations on the distribution of precipitation and cloudiness over a deforested area in Amazonia. Observed patterns of deforestation in Rondônia, Amazonia, with scales on the order of 10 km were used in this study to describe land surface conditions. Various simulations have been performed to identify the conditions under which the mesoscale circulations induced by the heterogeneous land surface could enhance cloudiness and local rainfall. The simulation results suggest that the synoptic forcing, in terms of atmospheric stability and background horizontal wind, dominates during the rainy season; synoptic conditions were so favorable to moist convection that the added effect of surface heterogeneity was negligible. During the dry season, a noticeable impact of mesoscale circulations resulting in enhancement of shallow clouds was simulated; the mesoscale circulations also triggered scattered deep convection that altered the spatial distribution of precipitation. During the break period, the transition from the rainy season to the dry season, the impact of mesoscale circulations on low-level clouds was evident only after reducing the magnitude of the background wind.
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
Abstract
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.
Abstract
The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.
Abstract
The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.
Hydrologic research at the interface between the atmosphere and land surface is undergoing a dramatic change in focus, driven by new societal priorities, emerging technologies, and better understanding of the earth system. In this paper an agenda for land surface hydrology research is proposed in order to open the debate for more comprehensive prioritization of science and application activities in the hydrologic sciences. Sets of priority science questions are posed and research strategies for achieving progress are identified. The proposed research agenda is also coupled with ongoing international data collection programs. The driving science questions and related research agenda lead to a call for the second International Hydrologic Decade. This activity will help to ensure that hydrology starts the new millennium as a coherent and vital discipline.
Hydrologic research at the interface between the atmosphere and land surface is undergoing a dramatic change in focus, driven by new societal priorities, emerging technologies, and better understanding of the earth system. In this paper an agenda for land surface hydrology research is proposed in order to open the debate for more comprehensive prioritization of science and application activities in the hydrologic sciences. Sets of priority science questions are posed and research strategies for achieving progress are identified. The proposed research agenda is also coupled with ongoing international data collection programs. The driving science questions and related research agenda lead to a call for the second International Hydrologic Decade. This activity will help to ensure that hydrology starts the new millennium as a coherent and vital discipline.
