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Bergaoui K, Fraj MB, Fragaszy S, Ghanim A, Hamadin O, Al-Karablieh E, Al-Bakri J, Fakih M, Fayad A, Comair F, Yessef M, Mansour HB, Belgrissi H, Arsenault K, Peters-Lidard C, Kumar S, Hazra A, Nie W, Hayes M, Svoboda M, McDonnell R. Development of a composite drought indicator for operational drought monitoring in the MENA region. Sci Rep 2024; 14:5414. [PMID: 38443431 PMCID: PMC10914844 DOI: 10.1038/s41598-024-55626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making-including aspects of salience, credibility, and legitimacy-within each national context.
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Affiliation(s)
- Karim Bergaoui
- International Water Management Institute (IWMI), Colombo, Sri Lanka.
- Dubai Technology Entrepreneur Campus, ACQUATEC Solutions, Dubai, UAE.
| | - Makram Belhaj Fraj
- International Water Management Institute (IWMI), Colombo, Sri Lanka
- Dubai Technology Entrepreneur Campus, ACQUATEC Solutions, Dubai, UAE
| | - Stephen Fragaszy
- International Water Management Institute (IWMI), Colombo, Sri Lanka.
| | - Ali Ghanim
- Drought Management Unit, Ministry of Water and Irrigation, Amman, Jordan
| | - Omar Hamadin
- Jordanian Meteorological Department, Ministry of Transportation, Amman, Jordan
| | - Emad Al-Karablieh
- Department of Agricultural Economics and Agribusiness, The University of Jordan, Amman, Jordan
| | - Jawad Al-Bakri
- Department of Land, Water and Environment, The University of Jordan, Amman, Jordan
| | - Mona Fakih
- Water Resources, General Directorate of Hydraulic and Electrical Resources, Ministry of Energy and Water, Beirut, Lebanon
| | - Abbas Fayad
- Water Resources, General Directorate of Hydraulic and Electrical Resources, Ministry of Energy and Water, Beirut, Lebanon
- Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, T1W 3G1, Canada
| | - Fadi Comair
- Water Resources, General Directorate of Hydraulic and Electrical Resources, Ministry of Energy and Water, Beirut, Lebanon
- Energy, Environment, and Water Research Centre in the Cyprus Institute, Nicosia, Cyprus
| | - Mohamed Yessef
- Institut Hassan II of Agronomy and Veterinary Medicine, Rabat, Morocco
| | | | | | - Kristi Arsenault
- Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA
- NASA Goddard Space Flight Center, Maryland, USA
| | | | - Sujay Kumar
- Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Abheera Hazra
- Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA
- NASA Goddard Space Flight Center, Maryland, USA
| | - Wanshu Nie
- Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Science Applications International Corporation, McLean, VA, USA
| | - Michael Hayes
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Mark Svoboda
- National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, NE, USA
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Ringerud S, Peters-Lidard C, Munchak J, You Y. Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrieval. J Atmos Ocean Technol 2021; 38:167-180. [PMID: 34054205 PMCID: PMC8151823 DOI: 10.1175/jtech-d-20-0048.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h-1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.
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Affiliation(s)
- Sarah Ringerud
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
- NASA Goddard Space Flight Center Greenbelt, Maryland
| | | | - Joe Munchak
- NASA Goddard Space Flight Center Greenbelt, Maryland
| | - Yalei You
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
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Foufoula-Georgiou E, Guilloteau C, Nguyen P, Aghakouchak A, Hsu KL, Busalacchi A, Turk FJ, Peters-Lidard C, Oki T, Duan Q, Krajewski W, Uijlenhoet R, Barros A, Kirstetter P, Logan W, Hogue T, Gupta H, Levizzani V. Advancing Precipitation Estimation, Prediction, and Impact Studies. Bull Am Meteorol Soc 2020; 101:E1584-E1592. [PMID: 34045766 PMCID: PMC8152147 DOI: 10.1175/bams-d-20-0014.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Affiliation(s)
- Efi Foufoula-Georgiou
- Department of Civil and Environmental Engineering, and Department of Earth System Science, University of California, Irvine, Irvine, California
| | - Clement Guilloteau
- Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
| | - Phu Nguyen
- Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
| | - Amir Aghakouchak
- Department of Civil and Environmental Engineering, and Department of Earth System Science, University of California, Irvine, Irvine, California
| | - Kuo-Lin Hsu
- Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
| | | | - F Joseph Turk
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
| | | | - Taikan Oki
- Integrated Research System for Sustainability Science, The University of Tokyo, Tokyo, Japan
| | | | | | - Remko Uijlenhoet
- Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, Netherlands
| | - Ana Barros
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina
| | | | - William Logan
- International Center for Water Resources Management, Alexandria, Virginia
| | - Terri Hogue
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado
| | - Hoshin Gupta
- Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
| | - Vincenzo Levizzani
- National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
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Uz SS, Ruane AC, Duncan BN, Tucker CJ, Huffman GJ, Mladenova IE, Osmanoglu B, Holmes TR, McNally A, Peters-Lidard C, Bolten JD, Das N, Rodell M, McCartney S, Anderson MC, Doorn B. Earth observations and integrative models in support of food and water security. Remote Sens Earth Syst Sci 2019; 2:18-38. [PMID: 33005873 PMCID: PMC7526267 DOI: 10.1007/s41976-019-0008-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 12/26/2018] [Accepted: 01/17/2019] [Indexed: 11/28/2022]
Abstract
Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
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Affiliation(s)
| | - Alex C. Ruane
- NASA Goddard Institute for Space Studies, Climate Impacts Group, New York, NY, USA
| | | | | | | | - Iliana E. Mladenova
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Amy McNally
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | | | | | - Narendra Das
- NASA Jet Propulsion Laboratory, Pasadena, CA, USA
| | | | - Sean McCartney
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
- Science Systems and Applications, Inc., Lanham, MD, USA
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You Y, Peters-Lidard C, Wang NY, Turk J, Ringerud S, Yang S, Ferraro R. The instantaneous retrieval of precipitation over land by temporal variation at 19 GHz. J Geophys Res Atmos 2018; 123:9279-9295. [PMID: 32832311 PMCID: PMC7440393 DOI: 10.1029/2017jd027596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 08/16/2018] [Indexed: 06/11/2023]
Abstract
The primary signal used in all current passive microwave precipitation retrieval algorithms over land is the depression of the instantaneous brightness temperature (TB) caused by ice scattering. This study presents a new methodology to retrieve instantaneous precipitation rate over land by using TB temporal variation (ΔTB) at 19 GHz, which primarily reflects the surface emissivity variation due to the precipitation impact. As a proof-of-concept, we exploit observations from five polar-orbiting satellites over the Southern Great Plains (SGP) of the United States. Results show that ΔTB at 19 GHz correlate well with the instantaneous precipitation rate. Further analysis shows that ΔTB at 19 GHz is better correlated with the precipitation rate when multiple satellite observations are used due to the much shorter re-visit time for a certain location. The retrieved instantaneous precipitation rate over SGP from ΔTB at 19 GHz reasonably agrees with the surface radar observations, with the correlation, the root mean square error and the bias being 0.49, 2.39 mm/hr and 6.54%, respectively. Future work seeks to combine the ice scattering signal at high frequencies and this surface emissivity variation signal at low frequencies to achieve an optimal retrieval performance.
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Affiliation(s)
- Yalei You
- Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites, University of Maryland, College Park, Maryland, USA
| | - Christa Peters-Lidard
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Nai-Yu Wang
- Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites, University of Maryland, College Park, Maryland, USA
| | - Joseph Turk
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - Sarah Ringerud
- Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites, University of Maryland, College Park, Maryland, USA
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Song Yang
- Naval Research Laboratory, Monterey, California, USA
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Lau WKM, Kim KM, Shi JJ, Matsui T, Chin M, Tan Q, Peters-Lidard C, Tao WK. Impacts of aerosol-monsoon interaction on rainfall and circulation over Northern India and the Himalaya Foothills. Clim Dyn 2017; 49:1945-1960. [PMID: 32801479 PMCID: PMC7427820 DOI: 10.1007/s00382-016-3430-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The boreal summer of 2008 was unusual for the Indian monsoon, featuring exceptional heavy loading of dust aerosols over the Arabian Sea and northern-central India, near normal all-India rainfall, but excessive heavy rain, causing disastrous flooding in the Northern Indian Himalaya Foothills (NIHF) regions, accompanied by persistent drought conditions in central and southern India. Using NASA Unified-physics Weather Research Forecast (NUWRF) model with fully interactive aerosol physics and dynamics, we carried out three sets of 7-day ensemble model forecast experiments: 1) control with no aerosol, 2) aerosol radiative effect only and 3) aerosol radiative and aerosol-cloud-microphysics effects, to study the impacts of aerosol-monsoon interactions on monsoon variability over the NIHF during the summer of 2008. Results show that aerosol-radiation interaction (ARI), i.e., dust aerosol transport, and dynamical feedback processes induced by aerosol-radiative heating, plays a key role in altering the large-scale monsoon circulation system, reflected by an increased north-south tropospheric temperature gradient, a northward shift of heavy monsoon rainfall, advancing the monsoon onset by 1-5 days over the HF, consistent with the EHP hypothesis (Lau et al. 2006). Additionally, we found that dust aerosols, via the semi-direct effect, increase atmospheric stability, and cause the dissipation of a developing monsoon onset cyclone over northeastern India/northern Bay of Bengal. Eventually, in a matter of several days, ARI transforms the developing monsoon cyclone into meso-scale convective cells along the HF slopes. Aerosol-Cloud-microphysics Interaction (ACI) further enhances the ARI effect in invigorating the deep convection cells and speeding up the transformation processes. Results indicate that even in short-term (up to weekly) numerical forecasting of monsoon circulation and rainfall, effects of aerosol-monsoon interaction can be substantial and cannot be ignored.
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Affiliation(s)
- William K M Lau
- Earth System Science Interdisciplinary Center (ESSIC), U. of Maryland, College Park, Md 20740
- Department of Atmospheric Sciences, U. of Texas, Station College, TX, 77843
| | - Kyu-Myong Kim
- Climate and Radiation Laboratory, Earth Science Division, Goddard Space Flight Center, Greenbelt, MD, 20771
| | - Jainn-Jong Shi
- Goddard Earth Science Technology, Application Research (GESTAR), Morgan State University, Baltimore, MD, 21251
| | - T Matsui
- Earth System Science Interdisciplinary Center (ESSIC), U. of Maryland, College Park, Md 20740
| | - M Chin
- Atmospheric Chemistry and Dynamics Laboratory, Earth Science Division, Goddard Space Flight Center, Greenbelt, MD, 20771
| | - Qian Tan
- Atmospheric Chemistry and Dynamics Laboratory, Earth Science Division, Goddard Space Flight Center, Greenbelt, MD, 20771
| | - C Peters-Lidard
- Earth Science Division, Goddard Space Flight Center, Greenbelt, MD, 20771
| | - W K Tao
- Mesoscale Atmospheric Processes Laboratory, Earth Science Division, Goddard Space Flight Center, Greenbelt, MD., 20771
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Getirana A, Peters-Lidard C, Rodell M, Bates PD. Tradeoff between cost and accuracy in large-scale surface water dynamic modeling. Water Resour Res 2017; 53:4942-4955. [PMID: 30078915 PMCID: PMC6069676 DOI: 10.1002/2017wr020519] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Recent efforts have led to the development of the local inertia formulation (INER) for an accurate but still cost-efficient representation of surface water dynamics, compared to the widely used kinematic wave equation (KINE). In this study, both formulations are evaluated over the Amazon basin in terms of computational costs and accuracy in simulating streamflows and water levels through synthetic experiments and comparisons against ground-based observations. Varying time steps are considered as part of the evaluation and INER at 60-second time step is adopted as the reference for synthetic experiments. Five hybrid (HYBR) realizations are performed based on maps representing the spatial distribution of the two formulations that physically represent river reach flow dynamics within the domain. Maps have fractions of KINE varying from 35.6% to 82.8%. KINE runs show clear deterioration along the Amazon river and main tributaries, with maximum RMSE values for streamflow and water level reaching 7827m3.s-1 and 1379cm near the basin's outlet. However, KINE is at least 25% more efficient than INER with low model sensitivity to longer time steps. A significant improvement is achieved with HYBR, resulting in maximum RMSE values of 3.9-292m3.s-1 for streamflows and 1.1-28.5cm for water levels, and cost reduction of 6-16%, depending on the map used. Optimal results using HYBR are obtained when the local inertia formulation is used in about one third of the Amazon basin, reducing computational costs in simulations while preserving accuracy. However, that threshold may vary when applied to different regions, according to their hydrodynamics and geomorphological characteristics.
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Affiliation(s)
- Augusto Getirana
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD
| | | | - Matthew Rodell
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD
| | - Paul D Bates
- School of Geographical Sciences, University of Bristol, Bristol, UK
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Tao J, Wu D, Gourley J, Zhang SQ, Crow W, Peters-Lidard C, Barros AP. Operational Hydrological Forecasting during the IPHEx-IOP Campaign - Meet the Challenge. J Hydrol (Amst) 2016; 541:434-456. [PMID: 30377386 PMCID: PMC6204264 DOI: 10.1016/j.jhydrol.2016.02.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 hours, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: 1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; 2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and 3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basin's outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15hrs was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware, configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.
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Affiliation(s)
- Jing Tao
- Dept. of Civil and Environmental Engineering, Duke University, Durham, NC
| | - Di Wu
- NASA/GSFC Mesoscale Atmospheric Processes Laboratory, Greenbelt, MD
- Science Systems and Applications, Inc., Lanham, MD
| | | | - Sara Q. Zhang
- NASA/GSFC Mesoscale Atmospheric Processes Laboratory, Greenbelt, MD
- Science Applications International Corporation, McLean, VA
| | - Wade Crow
- USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD
| | | | - Ana P. Barros
- Dept. of Civil and Environmental Engineering, Duke University, Durham, NC
- Corresponding Author: Dr. Ana P. Barros, , Phone: +1 919 660 5539
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Abstract
The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real time forecasts are conducted utilizing NASA-Unified Weather Research and Forecasting (NU-WRF) model to support the everyday weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are evaluated with Stage IV and Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE), with the objective to understand the impact of Land Surface initialization on the predicted precipitation. NU-WRF is also compared with North American Mesoscale Forecast System (NAM) 12 km forecast. In general, NU-WRF did a good job at capturing individual precipitation events. NU-WRF is also able to replicate a better rainfall spatial distribution compare with NAM. Further sensitivity tests show that the high-resolution makes a positive impact on rainfall forecast. The two sets of NU-WRF simulations produce very close rainfall characteristics. The Land surface initialization do not show significant impact on short-term rainfall forecast, and it is largely due to the soil conditions during the field campaign period.
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Affiliation(s)
- Di Wu
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
- Science Systems and Applications, Inc., Lanham, Maryland
| | - Christa Peters-Lidard
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
| | - Wei-Kuo Tao
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
| | - Walter Petersen
- Code 610.W, NASA GSFC/Wallops Flight Center, Wallops Island, Virginia
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Tao WK, Wu D, Lang S, Chern JD, Peters-Lidard C, Fridlind A, Matsui T. High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations. J Geophys Res Atmos 2016; 121:1278-1305. [PMID: 32802697 PMCID: PMC7427821 DOI: 10.1002/2015jd023986] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The Goddard microphysics was recently improved by adding a fourth ice class (frozen drops/hail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of ice/snow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.
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Affiliation(s)
- Wei-Kuo Tao
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Di Wu
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Science Systems and Applications, Inc., Lanham, Maryland, USA
| | - Stephen Lang
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Science Systems and Applications, Inc., Lanham, Maryland, USA
| | - Jiun-Dar Chern
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Goddard Earth Sciences Technology and Research Program, Morgan State University, Baltimore, Maryland, USA
| | - Christa Peters-Lidard
- Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Ann Fridlind
- NASA Goddard Institute for Space Studies, New York, New York, USA
| | - Toshihisa Matsui
- Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
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