The Weather Research and Forecasting (WRF) Model is designed to serve both operational forecasting and atmospheric research needs. It features two dynamic cores, multiple physical parameterizations, a variational data assimilation system, ability to couple with an ocean model, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications, including tropical storms.

Two robust configurations of WRF for tropical storms are the NOAA operational model Hurricane WRF (HWRF) and the National Center for Atmospheric Research (NCAR) Advanced Research Hurricane WRF (AHW). In this website users can obtain codes, datasets, and information for running both HWRF and AHW.

The Developmental Testbed Center and the Mesoscale and Microscale Meteorology (MMM) Division of NCAR support the use of all components of AHW and HWRF to the community, including the WRF atmospheric model with its Preprocessing System (WPS), various vortex initialization procedures, the Princeton Ocean Model for Tropical Cyclones (MPIPOM-TC), the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble-variational data assimilation system, the NOAA National Centers for Environmental Prediction (NCEP) coupler, the NOAA Geophysical Fluid Dynamics Laboratory (GFDL) Vortex Tracker, and various postprocessing and products utilities.

The effort to develop AHW has been a collaborative partnership, principally among NCAR, the Rosenstiel School at the University of Miami, and the Air Force Weather Agency (AFWA).

The effort to develop HWRF has been a collaborative partnership, principally between NOAA (NCEP, AOML, and GFDL) and the University of Rhode Island.

The community GSI system is a variational data assimilation system, designed to be flexible, state-of-art, and run efficiently on various parallel computing platforms. The GSI system is in the public domain and is freely available for community use.

The Developmental Testbed Center (DTC) currently maintains and supports a community version of the GSI system (now at Version 3.3). The testing and support of this GSI system at the DTC currently focus on regional numerical weather prediction (NWP) applications coupled with the Weather Research and Forecasting (WRF) Model , but the GSI can be applied to Global Forecast System(GFS) as well as other modelling systems.

The GSI version 3.3 GSI is an operational data assimilation system available for community use. Some of these GSI advanced features is listed as follows:

• Combined with an ensemble system, this version of GSI can be used as an ensemble-variational hybrid data assimilation system. One of an operational examples of such a capability is current NCEP's global data assimilation system (GDAS), implemented in Spring, 2012.
• Coupled with forecast models and their adjoint models, GSI can be turned into a four-dimensional variational (4D-Var) system.
• GSI features capabilities for observation sensitivity calculation. Coupled with its global model, this feature has been used by NASA for its operational data impact study.
• The observation operators in GSI can be used in an EnKF system or other data analysis systems, transforming model variables to observed variables at the observational space.

for a complete list of the new functions and changes included in each release version, as well as the observation data can be used in GSI, please check these links: version 3.3 ; version 3.2 ; version 3.1 .

Code management and community support for GSI and EnKF

The JNTP is currently supporting the Gridpoint Statistical Interpolation (GSI) system (variational and Ensemble-Variational (EnVar)) and the Ensemble Kalman Filter (EnKF) data assimilation system (ensemble based). Both are operational data assimilation systems used by multiple applications at NOAA, NASA, AF and other facilities and agencies (e.g., HWRF, GFS, RAP, NEMS, etc).

This project started in 2009 with a joint effort between the JNTP (under the auspices of the Developmental Testbed Center) and NCEP/EMC to expand the operational GSI data assimilation system to the research community, with the sponsorship of NOAA, AF and NCAR (supported by National Science Foundation (NSF)). This effort was expanded to the operational EnKF system in 2014. The objectives of this effort are to provide operational data assimilation capabilities to the research community, open the pathway for the research community to contribute directly to daily operations, and, eventually, accelerate transitions from research to operations, which is in line with the mission of the sponsors and the DTC. 

This effort has produced a code management framework to unify the distributed development and operational applications for GSI starting from 2010 and, additionally, EnKF starting from 2015. A joint data assimilation scientific steering and code review committee was formed, including major development teams for both systems across the United States. The GSI and EnKF systems and their supplemental libraries and auxiliary files are managed in the Community Data Assimilation Repository under version control (using Subversion). Targeted code tests are organized to maintain code robustness and integrity. General community support is provided through annual code releases, documentation, tutorials, a helpdesk, and assistance with code transitions and tests. Community researchers and users are encouraged to collaborate with the DTC and/or the JNTP to further advance GSI and associated data assimilation techniques, following the same code management procedures as internal developers. The User’s Page for GSI and EnKF can be found at the following links:

GSI

EnKF

Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for energy management, as is the need to further develop and implement advanced technologies. The National Center for Atmospheric Research (NCAR), a leader in atmospheric research, development and technology transfer for more than 50 years, is uniquely qualified to support the renewable energy industry in these endeavors.  NCAR scientists are already actively engaged with industry decision makers on how best to foresee and respond to short- and long-term changes in atmospheric conditions to mitigate risks associated with weather, particularly wind and solar energy prediction.

Although there has been a substantial, long-term effort by the weather research community to improve precipitation prediction, little attention has been paid to the prediction of clouds and insolation. The need for accurate insolation prediction is growing as the energy industry increases the percentage of distributed and concentrated solar energy. In addition, smart grid initiatives are expanding and the need for accurate forecasts of insolation (and temperature) is growing in parallel.

NCAR’s Joint Numerical Testbed (JNT) has a mission to test and verify the accuracy of weather models for NOAA and the research community. It is well positioned to assess the accuracy of operational and research models in predicting clouds and solar radiation. The results would highlight model skill and would provide valuable feedback to modelers on accuracy deficiencies. The results would be used to improve the models providing better information in the future to solar energy decision makers.

The Radar Icing Algorithm, or 'RadIA', utilizes the polarized moments from the National Weather Service's network of WSR-88D operational radars and the most recent Numerical Weather Prediction model temperature profiles to detect in-flight icing.  The algorithm consists of several meta-algorithms for various icing scenario inclusion or non-icing scenario exclusion, the sum of which are combined for a final in-flight icing product.  

Timely and accurate Quantitative Precipitation Estimates (QPE) are essential for forecasting stream flow particularly for flash floods and localized urban flooding.

Radar and rain Gauge based QPE is being used in this prediction system for determining:

• How hard it is raining
• How much rain has fallen in the intermediate past

QPE fields are being used in this prediction system:

• As input for quantitative precipitation nowcasting algorithms (Autonowcaster and Trident system)
• As input to the hydrology model (WRF-Hydro)
• For evaluation of radar-based QPE compared to rain gauge measurements of precipitation
• For verification of NWP precipitation forecasts

A variety of QPE techniques are being tested. Details of these techniques can be found at: Radar_QPE_processing.pdf

The primary goal is to develop an automated calibration platform for CONUS-wide watershed calibrations. This platform will allow for the quantification of the different sources of uncertainty on streamflow forecast skill.

Skill in model-based hydrologic forecasting depends on the ability to estimate the initial moisture and energy conditions of a watershed, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We are investigating these influences using a watershed simulation domain that spans the continental US (CONUS), encompassing a broad range of hydroclimatic variation, and that uses the current simulation models of National Weather Service (NWS) streamflow forecasting operations.

Two new postprocessing methods are proposed to reduce numerical weather prediction’s systematic and random errors. The first method consists of running a postprocessing algorithm inspired by the Kalman Filter (KF) through an ordered set of analog forecasts rather than a sequence of forecasts in time (ANKF). The analog of a forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast.

The second method is the weighted average of the observations that verified when the 10 best analogs were valid (AN). ANKF and AN are tested for 10-m wind speed predictions from the Weather Research and Forecasting (WRF) model, with observations from 400 surface stations over the western United States for a 6-month period. Both AN and ANKF predict drastic changes in forecast error (e.g., associated with rapid weather regime changes), a feature lacking in KF and a 7-day running-mean correction (7-Day).

The AN almost eliminates the bias of the raw prediction (Raw), while ANKF drastically reduces it with values slightly worse than KF. Both analog-based methods are also able to reduce random errors, therefore improving the predictive skill of Raw. The AN is consistently the best, with average improvements of 10%, 20%, 25%, and 35% with respect to ANKF, KF, 7-Day, and Raw, as measured by centered root-mean-square error, and of 5%, 20%, 25%, and 40%, as measured by rank correlation. Moreover, being a prediction based solely on observations, AN results in an efficient downscaling procedure that eliminates representativeness discrepancies between observations and predictions.