Environmental Modeling Center Research Articles

The G-IV Inner Circumnavigation: A Story of Successful Organic Interactions Between Research and Operations at NOAA

Abstract This study describes the research-to-operations process leading to a recent change in tropical cyclone (TC) reconnaissance sampling patterns as well as observing-system experiments that evaluated the impact of that change on numerical weather prediction model forecasts of TCs. A valuable part of this effort was having close, multipronged connections between the TC research and operational TC prediction communities at the National Oceanic and Atmospheric Administration (NOAA). Related to this work, NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and National Hurricane Center (NHC) have a long history of close collaboration to improve TC reconnaissance. Similar connections between AOML and NOAA’s Environmental Modeling Center (EMC) also laid a foundation for the observing-system experiments conducted here. More specifically, AOML and NHC collaborated in 2018 to change how NHC uses NOAA’s Gulfstream-IV (G-IV) jet during TC synoptic surveillance missions. That change added a second circumnavigation at approximately 1.5° from TC centers, when possible. Preliminary experiments suggest that the change improved track forecasts, though the intensity results are more mixed. Despite the somewhat small sample size over a 3-yr period, the track improvement does agree with prior work. This effort has led to additional work to more fully examine G-IV sampling strategies.

Improved forecast of 2015/16 El Niño event in an experimental coupled seasonal ensemble forecasting system

To improve NOAA’s seasonal forecasting capabilities, a new coupled system within the Unified Forecast System (UFS) framework is being developed through a community-wide effort led by NOAA’s Environmental Modeling Center targeting the configuration of a future operational Seasonal Forecast System (SFS v1). An experimental version of this ensemble seasonal forecasting system is tested on forecasting the strong El Niño of 2015/16. The then-operational systems and NCEP real-time seasonal forecasts (CFSv2) underestimated its strength towards the end of 2015 and beginning of 2016. In addition to perturbing the atmospheric initial conditions, run-time stochastic physics-based perturbations are applied in both atmosphere and ocean components of this new coupled system to represent the model uncertainties. The UFS ensembles are initialized on June 1st, 2015 and run through a 9-month period. Compared to CFSv2, the forecast of Niño 3.4 SST and intra-seasonal zonal windstress for the 2015/16 El Niño in the UFS system are improved, as is the ensemble spread. A cold SST forecast error develops in the central equatorial Pacific, likely from excess evaporative cooling, shallower thermocline, and an excessively strong vertical current shear driven cooling. Near the eastern equatorial Pacific coast, on the other hand, warm surface and cool subsurface errors persist from initialization until the end of the forecast. The results suggest that further improvement in the seasonal forecast may be achieved by a combination of factors, including, but not limited to, improving the coupled system initialization, along with the atmospheric physics.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)

Abstract. The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is on its way to deploying various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/, last access: 18 June 2022), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in collaboration between the Global Systems Laboratory, Chemical Science Laboratory, Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component version 2 (NGACv2) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in the GEFS is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke based on WRF-Chem was implemented. The GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL). The Blended Global Biomass Burning Emissions Product (GBBEPx version 3) provides biomass burning emission and fire radiative power (FRP) data. The global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid-scale transport and deposition are handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by the NCEP's operational FV3GFS. This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement and satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGACv2 system with the fundamental updates (e.g., dust and fire emission) in the atmospheric and chemical transport model.

Sub‐Seasonal Prediction of Drought and Streamflow Anomalies for Water Management in India

AbstractMeteorological and hydrologic prediction at short to sub‐seasonal scales is essential for reservoir operations to mitigate droughts. We examine the skills in the meteorological forecast from the Subseasonal Experiment (SubX) and Extended Range Forecast System (ERFS) for precipitation, maximum and minimum temperatures at 1, 7, 15, and 30 days lead. We bias‐corrected meteorological forecasts using the multivariate bias correction method for hydrologic prediction. The Variable Infiltration Capacity model was used to simulate total runoff and root‐zone soil moisture for India. We also developed a streamflow forecast for the five major river basins that have large reservoirs. Bias correction of meteorological forecast (precipitation, maximum and minimum temperatures) resulted in a considerable improvement in hydrologic and meteorological forecast skills. The Environmental Modeling Center (EMC) model from the SubX provides either better or equal forecast skills for the raw meteorological forecast compared to ERFS, which is an operational product in India. We examined the forecast skills of the meteorological and hydrological products for the two major droughts that occurred recently. We find that most forecast models effectively captured the onset, peak, and termination of the North Indian drought in 2015–2016 and the South Indian drought in 2016–2017 at a 30‐day lead. Bias correction of the meteorological forecast improved the streamflow forecast for the selected drought event upstream of the major reservoirs. The EMC model showed better forecast skills for the two major droughts than other forecast products. Overall, the SubX products show potential for short‐to‐sub‐seasonal scale hydrologic prediction that can assist water management in India.

Roles of TAO/TRITON and Argo in tropical Pacific observing system: An OSSE study for multiple time scale variability

AbstractIn this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the tropical Pacific observing system (TPOS) 2020 Project (TPOS 2020) which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1-meter near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no “observations” assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (>90 days), intraseasonal (20~90 days), and high-frequency (<20 days)]. It was found that (1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; (2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); (3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

A Quality Assessment of the Real-Time Mesoscale Analysis (RTMA) for Aviation

Abstract Missing observations at airports can cause delays in commercial and general aviation in the United States owing to Federal Aviation Administration (FAA) safety regulations. The Environmental Modeling Center (EMC) has provided interpolated temperature data from the National Oceanic and Atmospheric Administration’s Real-Time Mesoscale Analysis (RTMA) at airport locations throughout the United States since 2015, with these data substituting for missing temperature observations and mitigating impacts on air travel. A quality assessment of the RTMA is performed to determine if the RTMA could be used in a similar fashion for other weather observations, such as 10-m wind, ceiling, and visibility. Retrospective, data-denial experiments are used to perform the quality assessment by withholding observations from FAA-specified airports. Outliers seen in the RTMA ceiling and visibility analyses during events meeting or exceeding instrument flight rules suggest the RTMA should not be substituted for missing ceiling and visibility observations at this time. The RTMA is a suitable replacement for missing temperature observations for a subset of airports throughout most of the CONUS and Alaska, but not at all stations. Likewise, the RTMA is a suitable substitute for missing surface pressure observations at a subset of airports, with notable exceptions in regions of complex terrain. The RTMA may also be a suitable substitute for missing wind speed observations, provided the wind speed is ≤15 kt (1 kt ≈ 0.51 m s−1). Overall, these results suggest the potential for RTMA to substitute for additional missing observations while highlighting priority areas of future work for improving the RTMA.

Hurricane Model Development at GFDL: A Collaborative Success Story from a Historical Perspective

AbstractThe hurricane project at the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) was established in 1970. By the mid-1970s pioneering research had led to the development of a new hurricane model. As the reputation of the model grew, GFDL was approached in 1986 by the director of the National Meteorological Center about establishing a collaboration between the two federal organizations to transition the model into an operational modeling system. After a multiyear effort by GFDL scientists to develop a system that could support rigorous requirements of operations, and multiyear testing had demonstrated its superior performance compared to existing guidance products, operational implementation was made in 1995. Through collaboration between GFDL and the U.S. Navy, the model was also made operational at Fleet Numerical Meteorology and Oceanography Center in 1996. GFDL scientists continued to support and improve the model during the next two decades by collaborating with other scientists at GFDL, the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC),1 the National Hurricane Center, the U.S. Navy, the University of Rhode Island (URI), Old Dominion University, and the NOAA Hurricane Research Division. Scientists at GFDL, URI, and EMC collaborated to transfer key components of the GFDL model to the NWS new Hurricane Weather Research and Forecasting Model (HWRF) that became operational in 2007. The purpose of the article is to highlight the critical role of these collaborations. It is hoped that the experiences of the authors will serve as an example of how such collaboration can benefit the nation with improved weather guidance products.

HWRF Based Ensemble Prediction System Using Perturbations from GEFS and Stochastic Convective Trigger Function

ABSTRACT In this study, an ensemble prediction system (EPS) for the operational Hurricane Weather Research and Forecast (HWRF) model at the Environmental Modeling Center (EMC) of National Centers for Environmental Prediction (NCEP) is introduced and evaluated. The HWRF-EPS takes into account two main sources of uncertainties related to the initial/boundary conditions and the model physics by 1) using the large scale fields from NCEP Global Ensemble Forecast System (GEFS); and 2) stochastically perturbing the convective trigger function in the cumulus convection parameterization scheme. Verification for the 2011-2012 North Atlantic hurricane seasons shows that HWRF-EPS outperforms its deterministic versions at all lead times for both track and intensity forecast errors. Statistical characteristics are investigated and analyzed to demonstrate the effectiveness and robustness of the HWRF-EPS. The relationship between ensemble spread and forecast error for track and intensity in the HWRF-EPS indicated that the spread is likely more useful as a predictor of forecast error when it has moderately low values. Rank histogram analysis shows that the HWRF-EPS is well dispersed in both track and intensity forecasts except for the systematic errors inherited from the deterministic version. Further comparison with 2012 hurricane season’s top-flight models shows improved track and intensity forecasts from the HWRF-EPS.

High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment

AbstractTerabytes of weather data are generated every day by gridded model simulations and in situ and remotely sensed observations. With this accelerating accumulation of weather data, efficient computational solutions are needed to process, archive, and analyze the massive datasets. The Open Science Grid (OSG) is a consortium of computer resources around the United States that makes idle computer resources available for use by researchers in diverse scientific disciplines. The OSG is appropriate for high-throughput computing, that is, many parallel computational tasks. This work demonstrates how the OSG has been used to compute a large set of empirical cumulative distributions from hourly gridded analyses of the High-Resolution Rapid Refresh (HRRR) model run operationally by the Environmental Modeling Center of the National Centers for Environmental Prediction. These cumulative distributions derived from a 3-yr HRRR archive are computed for seven variables, over 1.9 million grid points, and each hour of the calendar year. The HRRR cumulative distributions are used to evaluate near-surface wind, temperature, and humidity conditions during two wildland fire episodes—the North Bay fires, a wildfire complex in Northern California during October 2017 that was the deadliest and costliest in California history, and the western Oklahoma wildfires during April 2018. The approach used here illustrates ways to discriminate between typical and atypical atmospheric conditions forecasted by the HRRR model. Such information may be useful for model developers and operational forecasters assigned to provide weather support for fire management personnel.

R2O Transition of NCAR’s Icing and Turbulence Algorithms into NCEP’s Operations

National Center for Environmental Prediction (NCEP) started distributing global operational gridded in flight icing, turbulence and convective cloud products as part of World Area Forecast System (WAFS) products in 2007. Simple algorithms were used to derive these products during early stage based on NCEP Global Forecast System (GFS) forecast. These products quickly became essential flight planning tool for international aviation community and are especially important to developing countries that do not have resource to run numerical models themselves. To further improve these products, Environmental Modeling Center (EMC) started collaborating with National Center for Atmospheric Research (NCAR) to transition their aviation research algorithms into NCEP’s operations (R2O), particularly Forecast Icing Potential (FIP) and Graphical Turbulence Guidance (GTG) algorithms. The initial attempt is to apply FIP to GFS forecast to potentially replace WAFS icing product. Extensive evaluation demonstrated FIP outperformed original WAFS icing product and, with support from Aviation Weather Center (AWC) and Federal Aviation Administration (FAA), EMC replaced US WAFS icing product with FIP in 2015. EMC recently also implemented GTG with 2017 GFS upgrade but GTG will not replace WAFS turbulence until 2019. This paper will describe the methodology which EMC used to transition NCAR’s aviation research algorithms into NCEP’s operations. It will also describe how EMC generates icing analysis data to be used as truth for performing objective verification. Several case studies will be presented and the methodology and results for objective validation will be discussed. Finally, future collaboration plan with NCAR and implementation plans to continue to improve WAFS products will be stated.

Flood Simulation in South Carolina Watersheds Using Different Precipitation Inputs

Flooding induced by extreme rainfall events causes tremendous loss of life and property and infrastructure failure. Accurate representation of precipitation, which has high variation in space and time, is critical to hydrologic model simulations and flood analyses. In this study, we examined responses of differently sized United States Geological Survey (USGS) hydrologic units to heavy precipitation using three different data sets. The first consists of rainfall observed at individual meteorological gauges. The second uses the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 km gridded radar-estimated precipitation (GRIB) Stage IV data. The third one derives from the method we developed that blends gauge data with the spatial coverage of the Parameter-elevation Relationships on Independent Slopes Model (PRISM) data. We examined how two watersheds in South Carolina respond to the three different representations of heavy rainfall, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) developed by the U.S. Army Corps of Engineers. We found that the latter two precipitation inputs that consider spatial representation of rainfall yielded similar performance and improved simulated streamflow as compared to simulation using rainfall observed at individual meteorological gauges. The method we developed overcomes the spatial sparsity of rain gauges required for interpolation and extends availability of precipitation surfaces. Our study advances the understanding of advantages and limitations of different precipitation products for flood simulation.

Sources of Bias in the Monthly CFSv2 Forecast Climatology

AbstractForecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

A Nonhydrostatic Multiscale Model on the Uniform Jacobian Cubed Sphere

The rapid expansion of contemporary computers is expected to enable operational integrations of global models of the atmosphere at resolutions close to 1 km, using tens of thousands of processors in the foreseeable future. Consequently, the algorithmic approach to global modeling of the atmosphere will need to change in order to better adjust to the new computing environment. One simple and convenient solution is to use low-order finite-differencing models, which generally require only local exchange of messages between processing elements, and thus are more compatible with the new computing environment. These models have already been tested with physics and are well established at high resolutions over regional domains. A global nonhydrostatic model, the Nonhydrostatic Multiscale Model on the B grid (NMMB), developed at the Environmental Modeling Center of the National Centers for Environmental Prediction during the first decade of this century is one such model. A drawback of the original version of global NMMB is that it is discretized on the standard longitude–latitude grid and requires application of Fourier polar filtering, which is relatively inefficient on massively parallel computers. This paper describes a reformulation of the NMMB on the grid geometry of a novel cubed sphere featuring a uniform Jacobian of the horizontal mapping, which provides a uniform resolution close to that of the equiangular gnomonic cubed sphere, but with a smooth transition of coordinates across the edges. The modeling approach and encountered challenges are discussed and several results are shown that demonstrate the viability of the approach.

Bridging Research to Operations Transitions: Status and Plans of Community GSI

Abstract With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.

Forecasting Tropical Cyclones in the Western North Pacific Basin Using the NCEP Operational HWRF: Real-Time Implementation in 2012

Abstract This study documents the recent efforts of the hurricane modeling team at the National Centers for Environmental Prediction’s (NCEP) Environmental Modeling Center (EMC) in implementing the operational Hurricane Weather Research and Forecasting Model (HWRF) for real-time tropical cyclone (TC) forecast guidance in the western North Pacific basin (WPAC) from May to December 2012 in support of the operational forecasters at the Joint Typhoon Warning Center (JTWC). Evaluation of model performance for the WPAC in 2012 reveals that the model has promising skill with the 3-, 4-, and 5-day track errors being 125, 220, and 290 nautical miles (n mi; 1 n mi = 1.852 km), respectively. Intensity forecasts also show good performance, with the most significant intensity error reduction achieved during the first 24 h. Stratification of the track and intensity forecast errors based on storm initial intensity reveals that HWRF tends to underestimate storm intensity for weak storms and overestimate storm intensity for strong storms. Further analysis of the horizontal distribution of track and intensity forecast errors over the WPAC suggests that HWRF possesses a systematic negative intensity bias, slower movement, and a rightward bias in the lower latitudes. At higher latitudes near the East China Sea, HWRF shows a positive intensity bias and faster storm movement. This appears to be related to underestimation of the dominant large-scale system associated with the western Pacific subtropical high, which renders weaker steering flows in this basin.

Community Support and Transition of Research to Operations for the Hurricane Weather Research and Forecasting Model

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) is an operational model used to provide numerical guidance in support of tropical cyclone forecasting at the National Hurricane Center. HWRF is a complex multicomponent system, consisting of the Weather Research and Forecasting (WRF) atmospheric model coupled to the Princeton Ocean Model for Tropical Cyclones (POM-TC), a sophisticated initialization package including a data assimilation system and a set of postprocessing and vortex tracking tools. HWRF’s development is centralized at the Environmental Modeling Center of NOAA’s National Weather Service, but it incorporates contributions from a variety of scientists spread out over several governmental laboratories and academic institutions. This distributed development scenario poses significant challenges: a large number of scientists need to learn how to use the model, operational and research codes need to stay synchronized to avoid divergence, and promising new capabilities need to be tested for operational consideration. This article describes how the Developmental Testbed Center has engaged in the HWRF developmental cycle in the last three years and the services it provides to the community in using and developing HWRF.

Dryline Position Errors in Experimental Convection-Allowing NSSL-WRF Model Forecasts and the Operational NAM

Abstract This study evaluates 24-h forecasts of dryline position from an experimental 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run daily at the National Severe Storms Laboratory (NSSL), as well as the 12-km grid-spacing North America Mesoscale Model (NAM) run operationally by the Environmental Modeling Center of NCEP. For both models, 0000 UTC initializations are examined, and for verification 0000 UTC Rapid Update Cycle (RUC) analyses are used. For the period 1 April–30 June 2007–11, 116 cases containing drylines in all three datasets were identified using a manual procedure that considered specific humidity gradient magnitude, temperature, and 10-m wind. For the 24-h NAM forecasts, no systematic east–west dryline placement errors were found, and the majority of the east–west errors fell within the range ±0.5° longitude. The lack of a systematic bias was generally present across all subgroups of cases categorized according to month, weather pattern, and year. In contrast, a systematic eastward bias was found in 24-h NSSL-WRF forecasts, which was consistent across all subgroups of cases. The eastward biases seemed to be largest for the subgroups that favored “active” drylines (i.e., those associated with a progressive synoptic-scale weather system) as opposed to “quiescent” drylines that tend to be present with weaker tropospheric flow and have eastward movement dominated by vertical mixing processes in the boundary layer.

Uncertainties in North American Land Data Assimilation Systems over the Contiguous United States

Abstract The Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP) and the University of Washington (UW) run parallel drought monitoring systems over the continental United States based on the North American Land Data Assimilation System (NLDAS). The NCEP system uses four land surface models (LSMs): Variable Infiltration Capacity (VIC), Noah, Mosaic, and Sacramento (SAC). The UW system uses VIC, SAC, Noah, and the Community Land Model (CLM). An assessment of differences in drought characteristics using both systems for the period 1979–2008 was performed. For soil moisture (SM) percentiles and runoff indices, differences are relatively small among different LSMs in the same system. However, the ensemble mean differences between the two systems are large over the western United States—in some cases exceeding 20% for SM and runoff percentile differences. These differences are most apparent after 2002 when the NCEP system transitioned to use the real-time North American Regional Reanalysis (NARR) and its precipitation gauge station data. (The UW system went into real-time operation in 2005.) Experiments were performed to address the sources of uncertainties. Comparison of simulations using the two systems with different model forcings indicates that the precipitation forcing differences are the primary source of the SM and runoff differences. While temperature, shortwave and longwave radiation, and wind speed forcing differences are also large after 2002, their contributions to SM and runoff differences are much smaller than precipitation.