Bradley T. Zavodsky

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

With support from NASAs Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. Satellite-resolved scales (roughly 1-25?km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles. NU-WRF is an observation-driven integrated land-atmosphere modeling system.The software is a NASA-oriented superset of the standard NCAR WRF software.Enhancements include a satellite simulator package, coupling and physics options.Maintained at NASA/GSFC in an SVN repository, software is available by agreement.Supports coupling studies for land, atmosphere, aerosols, clouds and precipitation.

Using sensor web protocols for environmental data acquisition and management

Abstract Standard interfaces for data and information access facilitate data management and usability by minimizing the effort required to acquire, catalog and integrate data from a variety of sources. The authors have prototyped several data management and analysis applications using Sensor Web Enablement Services, a suite of service protocols being developed by the Open Geospatial Consortium specifically for handling sensor data in near-real time. This paper provides a brief overview of some of the service protocols and describes how they are used in various sensor web projects involving near-real-time management of sensor data.

Improved Regional Analyses and Heavy Precipitation Forecasts With Assimilation of Atmospheric Infrared Sounder Retrieved Thermodynamic Profiles

This paper describes a procedure to assimilate Atmospheric Infrared Sounder (AIRS)-retrieved thermodynamic profiles into a regional configuration of the Weather Research and Forecasting (WRF) model and validates subsequent precipitation forecasts over the eastern half of the continental U.S. Quality indicators were used to select the highest quality temperature and moisture profiles for assimilation throughout the entire atmosphere in clear and partly cloudy regions and above cloud top in cloudy regions. Separate error characteristics for land and water profiles were also used in the assimilation process. Assimilation of AIRS profiles produced analyses with a better validation to in situ observations than the short-term WRF forecast first-guess field. The AIRS-enhanced initial conditions improved simulation of a severe weather event over Texas and Louisiana from February 12-13, 2007. For this event, assimilation of AIRS profiles produced a more unstable boundary-layer air mass in the warm sector ahead of an advancing midlatitude cyclone, resulting in enhanced convective available potential energy in the model. The simulated squall line and precipitation totals from a forecast initialized with AIRS-enhanced initial conditions more closely reflected ground-based observations than one initialized with a no-AIRS control forecast. The impact of the improved initial conditions through the assimilation of AIRS profiles was further demonstrated through an evaluation of a 37-day period from the winter of 2007. The unstable environment over the Gulf of Mexico and coastal region with the AIRS-enhanced initial conditions resulted in 20%+ improvements in the 6-h accumulated precipitation forecasts out to 48 h over that period.

Clouds in the Cloud: Weather Forecasts and Applications within Cloud Computing Environments

AbstractCloud computing offers new opportunities to the scientific community through cloud-deployed software, data-sharing and collaboration tools, and the use of cloud-based computing infrastructure to support data processing and model simulations. This article provides a review of cloud terminology of possible interest to the meteorological community, and focuses specifically on the use of infrastructure as a service (IaaS) concepts to provide a platform for regional numerical weather prediction. Special emphasis is given to developing countries that may have limited access to traditional supercomputing facilities. Amazon Elastic Compute Cloud (EC2) resources were used in an IaaS capacity to provide regional weather simulations with costs ranging from

Assimilation of SMOS Retrievals in the Land Information System

40 to

Neural Network Estimation of Atmospheric Thermodynamic State for Weather Forecasting Applications

75 per 48-h forecast, depending upon the configuration. Simulations provided a reasonable depiction of sensible weather elements and precipitation when compared against typical validation data available over Central America and the Caribbean.

A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in roughly the upper 5 cm with a 30-50-km resolution and a mission accuracy requirement of 0.04 cm3/cm-3. These observations can be used to improve land surface model (LSM) soil moisture states through data assimilation (DA). In this paper, SMOS soil moisture retrievals are assimilated into the Noah LSM via an Ensemble Kalman Filter within the National Aeronautics and Space Administration Land Information System. Bias correction is implemented using cumulative distribution function (cdf) matching, with points aggregated by either land cover or soil type to reduce the sampling error in generating the cdfs. An experiment was run for the warm season of 2011 to test SMOS DA and to compare assimilation methods. Verification of soil moisture analyses in the 0-10-cm upper layer and the 0-1-m root zone was conducted using in situ measurements from several observing networks in central and southeastern United States. This experiment showed that SMOS DA significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10-cm layer. Time series at specific stations demonstrates the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared with using a simple uniform correction curve.

Development and Application of Atmospheric Infrared Sounder Ozone Retrieval Products for Operational Meteorology

We present recent work using neural network estimation techniques to process satellite observation of the Earth’s atmosphere to improve weather forecasting performance. A novel statistical method for the retrieval of atmospheric temperature and moisture (relative humidity) profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of stochastic cloud clearing mechanisms together with neural network estimation. The algorithm outputs are ingested into a numerical model, and forecast information and decision support tools are then presented to a meteorologist. We discuss the underlying physical problem, the algorithmic framework, and the interaction with forecaster.

Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

Snowfall rate retrieval from space-borne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in-cloud precipitation. A physically-based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit-A (AMSU-A)/Microwave Humidity Sounder (MHS) sensor pair and the Advanced Technology Microwave Sounder (ATMS). Currently, these instruments are aboard five polar-orbiting satellites, namely NOAA-18, NOAA-19, Metop-A, Metop-B, and Suomi-NPP. The SFR algorithm relies on a separate snowfall detection (SD) algorithm that is composed of a satellite-based statistical model and a set of numerical weather prediction (NWP) model-based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content (IWC) adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one-dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real-time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR towards radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the Contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration (NOAA) and can be obtained from that organization.

Overview of the NASA TROPICS CubeSat Constellation Mission [STUB]

The National Aeronautics and Space Administration Short-term Prediction Research and Transition (SPoRT) Center has worked closely with the Geostationary Operational Environmental Satellite-R series and the Joint Polar Satellite System Proving Grounds to develop and transition unique ozone products derived from Atmospheric Infrared Sounder (AIRS) ozone retrievals to the Ocean Prediction Center (OPC). These products were developed to aid identification of stratospheric air and enhance situational awareness of rapid cyclogenesis and hurricane-force wind events during which stratospheric air may play a key role. OPC forecasters have used the European Organisation for the Exploitation of Meteorological Satellites Meteosat Spinning Enhanced Visible and Infrared Imager red, green, blue (RGB) air mass imagery to identify regions of stratospheric air for their unique weather forecasting challenges; however, the qualitative nature of the new RGB product facilitated a need for quantitative products to enhance forecaster confidence in the RGB air mass imagery. To enhance forecaster interpretation and confidence in the RGB air mass imagery, SPoRT created the total column ozone and ozone anomaly products from hyperspectral infrared sounder ozone retrievals. AIRS Version 6 Level-2 ozone retrievals were utilized to create hourly ozone products over a northwest hemisphere domain. An example case study from February 24-27, 2014, shows the utility of the ozone products in enhancing interpretation of the RGB air mass imagery for anticipating rapid cyclogenesis and hurricane-force winds in the North Atlantic.