Environmental Modeling Center

Dunkirk Town Center, MD, United States

Environmental Modeling Center

Dunkirk Town Center, MD, United States

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Wen C.,5830 University Research Court | Xue Y.,5830 University Research Court | Kumar A.,5830 University Research Court | Behringer D.,Environmental Modeling Center | Yu L.,Woods Hole Oceanographic Institution
Climate Dynamics | Year: 2017

NCEP/DOE reanalysis (R2) and Climate Forecast System Reanalysis (CFSR) surface fluxes are widely used by the research community to understand surface flux climate variability, and to drive ocean models as surface forcings. However, large discrepancies exist between these two products, including (1) stronger trade winds in CFSR than in R2 over the tropical Pacific prior 2000; (2) excessive net surface heat fluxes into ocean in CFSR than in R2 with an increase in difference after 2000. The goals of this study are to examine the sensitivity of ocean simulations to discrepancies between CFSR and R2 surface fluxes, and to assess the fidelity of the two products. A set of experiments, where an ocean model was driven by a combination of surface flux components from R2 and CFSR, were carried out. The model simulations were contrasted to identify sensitivity to different component of the surface fluxes in R2 and CFSR. The accuracy of the model simulations was validated against the tropical moorings data, altimetry SSH and SST reanalysis products. Sensitivity of ocean simulations showed that temperature bias difference in the upper 100 m is mostly sensitive to the differences in surface heat fluxes, while depth of 20 °C (D20) bias difference is mainly determined by the discrepancies in momentum fluxes. D20 simulations with CFSR winds agree with observation well in the western equatorial Pacific prior 2000, but have large negative bias similar to those with R2 winds after 2000, partly because easterly winds over the central Pacific were underestimated in both CFSR and R2. On the other hand, the observed temperature variability is well reproduced in the tropical Pacific by simulations with both R2 and CFSR fluxes. Relative to the R2 fluxes, the CFSR fluxes improve simulation of interannual variability in all three tropical oceans to a varying degree. The improvement in the tropical Atlantic is most significant and is largely attributed to differences in surface winds. © 2017 Springer-Verlag Berlin Heidelberg


Chen T.-C.,Iowa State University | Yen M.-C.,National Central University | Tsay J.-D.,Iowa State University | Thanh N.T.T.,Aerospace Meteorological Observatory | Alpert J.,Environmental Modeling Center
Monthly Weather Review | Year: 2012

The 30-31 October 2008 Hanoi, Vietnam, heavy rainfall-flood (HRF) event occurred unusually farther north than other Vietnam events. The cause of this event is explored with multiple-scale processes in the context of the midlatitude-tropical interaction. In the midlatitudes, the cold surge linked to the Hanoi event can be traced westward to the leeside cyclogenesis between the Altai Mountains and Tianshan. This cyclone developed into a Bering Sea explosive cyclone later, simultaneously with the occurrence of the Hanoi HRF event. In the tropics, a cold surge vortex formed on 26 October, south of the Philippines, through the interaction of an easterly disturbance, an already existing small surface vortex in the Celebes Sea, and the eastern Asian cold surge flow. This cold surge vortex developed into a cyclone, juxtaposed with the surface high of the cold surge flow, and established a strong moist southeasterly flow from the South China Sea to Hanoi, which helped maintain the HRF event. Spectral analysis of the zonal winds north and south of the Hanoi HRF cyclone and rainfall at Hanoi reveal the existence of three monsoon modes: 30-60, 12-24, and 5 days. The cold surge vortex developed into an HRF cyclone in conjunction with the in-phase constructive interference of the three monsoon modes, while the Hanoi HRF event was hydrologically maintained by the northwestward flux of water vapor into Hanoi by these monsoon modes. © 2012 American Meteorological Society.


Hamill T.M.,National Oceanic and Atmospheric Administration | Whitaker J.S.,National Oceanic and Atmospheric Administration | Kleist D.T.,Environmental Modeling Center | Fiorino M.,National Oceanic and Atmospheric Administration | Benjamin S.G.,National Oceanic and Atmospheric Administration
Monthly Weather Review | Year: 2011

Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009's T384L64 (~31 km at 25°N) to 2010's T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method. The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), theMetOffice (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic andAtmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation. © 2011 American Meteorological Society.


Snyder A.D.,University of Utah | Pu Z.,University of Utah | Zhu Y.,Environmental Modeling Center
Weather and Forecasting | Year: 2010

This study evaluates the performance of the NCEP global ensemble forecast system in predicting the genesis and evolution of five named tropical cyclones and two unnamed nondeveloping tropical systems during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) between August and September 2006. The overall probabilities of the ensemble forecasts of tropical cyclone genesis are verified relative to a genesis time defined to be the first designation of the tropical depression from the National Hurricane Center (NHC). Additional comparisons are also made with high-resolution deterministic forecasts from the NCEP Global Forecast System (GFS). It is found that the ensemble forecasts have high probabilities of genesis for the three strong storms that formed from African easterly waves, but failed to accurately predict the pregenesis phase of two weaker storms that formed farther west in the Atlantic Ocean. The overall accuracy for the genesis forecasts is above 50% for the ensemble forecasts initialized in the pregenesis phase. The forecast uncertainty decreases with the reduction of the forecast lead time. The probability of tropical cyclone genesis reaches nearly 90% and 100% for the ensemble forecasts initialized near and in the postgenesis phase, respectively. Significant improvements in the track forecasts are found in the ensemble forecasts initialized in the postgenesis phase, possibly because of the implementation of the NCEP storm relocation scheme, which provides an accurate initial storm position for all ensemble members. Even with coarser resolution (T126L28 for the ensemble versus T384L64 for the GFS), the overall performance of the ensemble in predicting tropical cyclone genesis is compatible with the high-resolution deterministic GFS. In addition, false alarm rates for nondeveloping waves were low in both the GFS and ensemble forecasts. © 2010 American Meteorological Society.


Brill K.F.,Hydrometeorological Prediction Center | Pyle M.,Environmental Modeling Center
Weather and Forecasting | Year: 2010

Critical performance ratio (CPR) expressions for the eight conditional probabilities associated with the 2 × 2 contingency table of outcomes for binary (dichotomous "yes" or "no") forecasts are derived. Two are shown to be useful in evaluating the effects of hedging as it approaches random change. The CPR quantifies how the probability of detection (POD) must change as frequency bias changes, so that a performance measure (or conditional probability) indicates an improved forecast for a given value of frequency bias. If yes forecasts were to be increased randomly, the probability of additional correct forecasts (hits) is given by the detection failure ratio (DFR). If the DFR for a performance measure is greater than the CPR, the forecast is likely to be improved by the random increase in yes forecasts. Thus, the DFR provides a benchmark for the CPR in the case of frequency bias inflation. If yes forecasts are decreased randomly, the probability of removing a hit is given by the frequency of hits (FOH). If the FOH for a performance measure is less than the CPR, the forecast is likely to be improved by the random decrease in yes forecasts. Therefore, the FOH serves as a benchmark for the CPR if the frequency bias is decreased. The closer the FOH (DFR) is to being less (greater) than or equal to the CPR, the more likely it may be to enhance the performance measure by decreasing (increasing) the frequency bias. It is shown that randomly increasing yes forecasts for a forecast that is itself better than a randomly generated forecast can improve the threat score but is not likely to improve the equitable threat score. The equitable threat score is recommended instead of the threat score whenever possible. © 2010 American Meteorological Society.


Livneh B.,University of Washington | Xia Y.,Environmental Modeling Center | Mitchell K.E.,Environmental Modeling Center | Ek M.B.,Environmental Modeling Center | Lettenmaier D.P.,University of Washington
Journal of Hydrometeorology | Year: 2010

A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model's albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales. © 2010 American Meteorological Society.


Yang R.,Environmental Modeling Center | Yang R.,I-Systems | Mitchell K.,Environmental Modeling Center | Meng J.,Environmental Modeling Center | And 2 more authors.
Journal of Climate | Year: 2011

To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)'s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP-Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis. Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June-August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same NoahLSMas utilized in the land component of the CFS, while improper initializations of the NoahLSMusing the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions. © 2011 American Meteorological Society.


Wei H.,M Group Inc. | Xia Y.,M Group Inc. | Mitchell K.E.,Environmental Modeling Center | Ek M.B.,Environmental Modeling Center
Hydrological Processes | Year: 2013

The Noah model is a land surface model of the National Centers for Environmental Prediction. It has been widely used in regional coupled weather and climate models (i.e. Weather Research and Forecasting Model, Eta Mesoscale Model) and global coupled weather and climate models (i.e. National Centers for Environmental Prediction Global Forecast System, Climate Forecast System). Therefore, its continued improvement and development are keys to enhancing our weather and climate forecast ability and water and energy flux simulation accuracy. North American Land Data Assimilation System phase 1 (NLDAS-1) experiments indicated that the Noah model exhibited substantial bias in latent heat flux, total runoff and land skin temperature during the warm season, and such bias can significantly affect coupled weather and climate models. This paper presents a study to improve the Noah model by adding model parameterization processes such as including seasonal factor on leaf area index and root distribution and selecting optimal model parameters. We compared simulated latent heat flux, mean annual runoff and land skin temperature from the Noah control and test versions with measured latent heat flux, land surface skin temperature, mean annual runoff and satellite-retrieved land surface skin temperature. The results show that the test version significantly reduces biases in latent heat, total runoff and land skin temperature simulation. The test version has been used for the NLDAS phase 2 (NLDAS-2) to produce 30-year water flux, energy flux and state variable products to support the US drought monitor of National Integrated Drought Information System. © 2012 John Wiley & Sons, Ltd.


Wang W.,Climate Prediction Center | Xie P.,Climate Prediction Center | Yoo S.-H.,Climate Prediction Center | Xue Y.,Climate Prediction Center | And 2 more authors.
Climate Dynamics | Year: 2011

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately. © 2010 Springer-Verlag (outside the USA).


Castro C.L.,University of Arizona | Chang H.-I.,University of Arizona | Dominguez F.,University of Arizona | Carrillo C.,University of Arizona | And 2 more authors.
Journal of Climate | Year: 2012

Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982-2000) and the corresponding NCEP-NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for highresolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scaleatmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscalingwill add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard. © 2012 American Meteorological Society.

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