Challenges of operational river forecasting
Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover...
Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States
This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value di...
A preliminary synthesis of major scientific results during the SALSA program
The objective of this paper is to provide an overview of the primary results of the Semi-Arid Land-Surface-Atmosphere (SALSA) Program in the context of improvements to our overall understanding of hydrologic, ecologic, and atmospheric processes and their interactions in a semi-arid basin. The major...
A preliminary synthesis of major scientific results during the SALSA program
The objective of this paper is to provide an overview of the primary results of the Semi-Arid Land-Surface-Atmosphere (SALSA) Program in the context of improvements to our overall understanding of hydrologic, ecologic, and atmospheric processes and their interactions in a semi-arid basin. The major...
Challenges of operational river forecasting
International audience ; Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and i...
Evaluation of NASA's MERRA precipitation product in reproducing the observed trend and distribution of extreme precipitation events in the United States
This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value di...
Evaluation of NASA's MERRA precipitation product in reproducing the observed trend and distribution of extreme precipitation events in the United States
© 2016 American Meteorological Society. This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-inv...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting
This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/ gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting
This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based s...
Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS
Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data f...
The PERSIANN family of global satellite precipitation data: a review and evaluation of products
Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and...
How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts?
Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of c...
Bias adjustment of satellite precipitation estimation using ground-based measurement: A case study evaluation over the southwestern United States
Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedu...
Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks
Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the...
Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information
Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical...
Diurnal Variability of Tropical Rainfall Retrieved from Combined GOES and TRMM Satellite Information
Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical...
The PERSIANN family of global satellite precipitation data: A review and evaluation of products
Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and...
Advancing the remote sensing of precipitation
Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in...
Assessing the impacts of different WRF precipitation physics in hurricane simulations
Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of...
Assessing the impacts of different WRF precipitation physics in hurricane simulations
Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting
This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based s...
Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau
On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitabl...
A modified soil adjusted vegetation index
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by account...
A Systematic Review of the Safety of Lisdexamfetamine Dimesylate
BACKGROUND: Here we review the safety and tolerability profile of lisdexamfetamine dimesylate (LDX), the first long-acting prodrug stimulant for the treatment of attention-deficit/hyperactivity disorder (ADHD). METHODS: A PubMed search was conducted for English-language articles published up to 16 S...
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecti...
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