Potential Synergies Between TROPICS and GOES-R

No abstract availabl

Demonstrating the Operational Value of Thermodynamic Hyperspectral Profiles in the Pre-Convective Environment

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) Weather Forecasting Offices (WFO). As a part of the transition to operations process, SPoRT attempts to identify possible limitations in satellite observations and provide operational forecasters a product that will result in the most impact on their forecasts. One operational forecast challenge that some NWS offices face, is forecasting convection in data-void regions such as large bodies of water. The Atmospheric Infrared Sounder (AIRS) is a sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. This paper will demonstrate an approach to assimilate AIRS profile data into a regional configuration of the WRF model using its three-dimensional variational (3DVAR) assimilation component to be used as a proxy for the individual profiles

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts

P161 Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

For over 6 years, AIRS radiances have been assimilated operationally into National (e.g. Environmental Modeling Center (EMC)) and International (e.g. European Centre for Medium-Range Weather Forecasts (ECMWF)), operational centers; assimilated in the North American Mesoscale (NAM) since 2008. Due partly to data latency and operational constraints, hyperspectral radiance assimilation has had less impact on the Gridpoint Statistical Interpolation (GSI) system used in the NAM and GFS. Objective of this project is to use AIRS retrieved profiles as a proxy for the AIRS radiances in situations where AIRS radiances are unable to be assimilated in the current operational system by evaluating location and magnitude of analysis increments

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths

Transitioning NASA and NOAA Satellite Products, Modeling Data Assimilation Techniques, and Nowcasting Tools to Operations

No abstract availabl

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes applicationdriven research to provide a fundamental understanding of how SMAP data products will be used to improve decisionmaking at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a realtime regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warmseason months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive Lband radiometer that is used to retrieve surface soil moisture at 35km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive Lband instrument in conjunction with a 3km resolution active radar component of slightly degraded accuracy. A combined radarradiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model (LSM) simulations and includes an Ensemble Kalman Filter for conducting land surface DA. SPoRT has added a module to read, qualitycontrol and biascorrect swaths of Level II SMOS soil moisture retrievals prior to assimilation within LIS. The impact of SMOS DA is being tested using the Noah LSM. Experiments are being conducted to examine the impacts of SMOS soil moisture DA on the resulting LISNoah fields and subsequent NWP simulations using the Weather Research and Forecasting (WRF) model initialized with LISNoah output. LISNoah soil moisture will be validated against in situ observations from Texas A&M's North American Soil Moisture Database to reveal the impact and possible improvement in soil moisture trends through DA. WRF model NWP case studies will test the impacts of DA on the simulated nearsurface and boundarylayer environments, and precipitation during both quiescent and disturbed weather scenarios. Emphasis will be placed on cases with large analysis increments, especially due to contributions from regional irrigation patterns that are not represented by precipitation input in the baseline LISNoah run. This poster presentation will describe the soil moisture DA methodology and highlight LISNoah and WRF simulation results with and without assimilation

Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

No abstract availabl

Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

The NASA SPoRT Center is working to incorporate SuomiNPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the landatmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer prefrontal convection case. Most regional NWP applications make use of a monthly GVF climatology for use in the Noah LSM within the Weather Research and Forecasting (WRF) model. The GVF partitions incoming energy into direct surface heating/evaporation over bare soil versus evapotranspiration processes over vegetated surfaces. Misrepresentations of the fractional coverage of vegetation during anomalous weather/climate regimes (e.g., early/late bloom or freeze; drought) can lead to poor NWP model results when landatmosphere feedback is important. SPoRT has been producing a daily MODIS GVF product based on the University of Wisconsin Direct Broadcast swaths of Normalized Difference Vegetation Index (NDVI). While positive impacts have been demonstrated in the WRF model for some cases, the reflectances composing these NDVI do not correct for atmospheric aerosols nor satellite view angle, resulting in temporal noisiness at certain locations (especially heavy vegetation). The method behind the NESDIS VIIRS GVF is expected to alleviate the issues seen in the MODIS GVF realtime product, thereby offering a higherquality dataset for modeling applications. SPoRT is evaluating the VIIRS GVF data against the MODIS realtime and climatology GVF in both WRF and the NASA Land Information System. SPoRT has a history of assimilating hyperspectral infrared retrieved profile