Stephanie Granger

AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases

Abstract The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAAs requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO2, CO, CH4, O3, SO2, and aerosols. The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECM...

Formulation and validation of simulated data for the Atmospheric Infrared Sounder (AIRS)

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data under-represent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.

Satellite-Based Precipitation Estimation and Its Application for Streamflow Prediction over Mountainous Western U.S. Basins

AbstractRecognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation ove...

Probabilistic Seasonal Prediction of Meteorological Drought Using the Bootstrap and Multivariate Information

AbstractIn the present work, a probabilistic ensemble method using the bootstrap is developed to predict the future state of the standard precipitation index (SPI) commonly used for drought monitoring. The methodology is data driven and has the advantage of being easily extended to use more than one variable as predictors. Using 110 years of monthly observations of precipitaton, surface air temperature, and the Nino-3.4 index, the method was employed to assess the impact of the different variables in enhancing the prediction skill. A predictive probability density function (PDF) is produced for future 6-month SPI, and a log-likelihood skill score is used to cross compare various combination scenarios using the entire predictive PDF and with reference to the observed values set aside for validation. The results suggest that the multivariate prediction using complementary information from 3- and 6-month SPI and initial surface air temperature significantly improves seasonal prediction skills for capturing d...

Early detection of drought onset using near surface temperature and humidity observed from space

ABSTRACT Drought is associated with severe societal impacts ranging from shortages of water for human consumption to agricultural failure and famine. An important aspect of drought forecast is determining the onset, which is critical for early warning efforts and water resources and agriculture planning. Indices of precipitation shortage have been widely used to detect the onset of drought because precipitation deficits often lead to shortages in other hydrologic variables such as soil moisture and runoff. The present work demonstrates that atmospheric temperature and humidity observations from the Atmospheric Infrared Sounder (AIRS) contain information that can be used to detect drought onset earlier than that obtained from precipitation deficit. By calculating the standardized indices for precipitation, near-surface temperature, vapour pressure deficit, and relative humidity, we show that in many regions of the world signals of drought onset can be detected from near-surface temperature and humidity data a few months earlier than those obtained from precipitation deficit. In particular, vapour pressure deficit showed higher effectiveness than relative humidity or temperature only. The outcome was generally consistent for the three- and six-month accumulations studied here. Further analysis using 65 years (1960–2014) of monthly temperature and humidity data derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data set over the continental United States suggests that there is a good agreement between drought early detection signals obtained from AIRS and that from ground stations during the overlapped (2003–2014) period. Analysis using longer record suggests that the frequency of successful early detection of drought onset using temperature and humidity data shows regional shift towards eastern United States in the recent years.

A Global Capacity Building Vision for Societal Applications of Earth Observing Systems and Data: Key Questions and Recommendations

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

Development of Level 3 (gridded) products for the Atmospheric Infrared Sounder (AIRS)

The Atmospheric Infrared Sounder (AIRS) sounding system is a suite of infrared and microwave instruments flown as part of NASAs Earth Observing System (EOS) onboard the Aqua platform. The AIRS dataset provides a daily, global view of Earth processes at a finer vertical resolution than ever before. However, analysis of the AIRS data is a daunting task given the sheer volume and complexity of the data. The volume of data produced by the EOS project is unprecedented; the AIRS project alone will produce many terabytes of data over the lifetime of the mission. This paper describes development of AIRS Level 3 data products that will help to alleviate problems of access and usability.

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications.