Engineering topics

Hou, Arthur

Publications and source records attributed to Hou, Arthur.

32 records · Page 2

NASA NTRS2004

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

Atlas, Robert↗

NASA NTRS2004

Low Frequency Oscillations in Assimilated Global Datasets Using TRMM Rainfall Observations

Global datasets for the period May-August 1998 from the Goddard Earth Observing System (GEOS) data assimilation system (DAS) with/without assimilated Tropical Rainfall Measuring Mission (TRMM) precipitation are analyzed against European Center for Medium-Range Weather Forecast (ECMWF) output, NOAA observed outgoing longwave radiation (OLR) data, and TRMM measured rainfall. The purpose of this study is to investigate the representation of the Madden-Julian Oscillation (MJO) in GEOS assimilated global datasets, noting the impact of TRMM observed rainfall on the MJO in GEOS data assimilations. A space-time analysis of the OLR data indicates that the observed OLR exhibits a spectral maximum for eastward-propagating wavenumber 1-3 disturbances with periods of 20-60 days in the 0deg-30degN latitude band. The assimilated OLR has a similar feature but with a smaller magnitude. However, OLR spectra from assimilations including TRMM rainfall data show better agreement with observed OLR spectra than spectra from assimilations without TRMM rainfall. Similar results are found for wavenumber 4-6 disturbances. There is a spectral peak for eastward-propagating wavenumber 4-6 disturbances with periods of 20-40 days near the equator, while for westward-moving disturbances, a spectral peak is noted for periods of 30-50 days near 25degN. To isolate the MJO, a 30-50 day band filter is selected for this study. It was found that the eastward-propagating waves from the band-filtered observed OLR between 10degs- 10degN are located in the eastern hemisphere. Similar patterns are evident in surface rainfall and the 850 hPa wind field. Assimilation of TRMM-observed rainfall reveals more distinct MJO features in the analysis than without rainfall assimilation. Similar analyses are also conducted over the Indian summer monsoon and East Asia summer monsoon regions, where the MJO is strongly related to the summer monsoon active-break patterns.

Tao, Li↗

NASA NTRS2004

Exploring New Pathways in Precipitation Assimilation

Precipitation assimilation poses a special challenge in that the forward model for rain in a global forecast system is based on parameterized physics, which can have large systematic errors that must be rectified to use precipitation data effectively within a standard statistical analysis framework. We examine some key issues in precipitation assimilation and describe several exploratory studies in assimilating rainfall and latent heating information in NASA's global data assimilation systems using the forecast model as a weak constraint. We present results from two research activities. The first is the assimilation of surface rainfall data using a time-continuous variational assimilation based on a column model of the full moist physics. The second is the assimilation of convective and stratiform latent heating retrievals from microwave sensors using a variational technique with physical parameters in the moist physics schemes as a control variable. We will show the impact of assimilating these data on analyses and forecasts. Among the lessons learned are (1) that the time-continuous application of moisture/temperature tendency corrections to mitigate model deficiencies offers an effective strategy for assimilating precipitation information, and (2) that the model prognostic variables must be allowed to directly respond to an improved rain and latent heating field within an analysis cycle to reap the full benefit of assimilating precipitation information. of microwave radiances versus retrieval information in raining areas, and initial efforts in developing ensemble techniques such as Kalman filter/smoother for precipitation assimilation. Looking to the future, we discuss new research directions including the assimilation

Hou, Arthur↗

NASA NTRS2002

Understanding the Global Water and Energy Cycle Through Assimilation of Precipitation-Related Observations: Lessons from TRMM and Prospects for GPM

Understanding the Earth's climate and how it responds to climate perturbations relies on what we know about how atmospheric moisture, clouds, latent heating, and the large-scale circulation vary with changing climatic conditions. The physical process that links these key climate elements is precipitation. Improving the fidelity of precipitation-related fields in global analyses is essential for gaining a better understanding of the global water and energy cycle. In recent years, research and operational use of precipitation observations derived from microwave sensors such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) have shown the tremendous potential of using these data to improve global modeling, data assimilation, and numerical weather prediction. We will give an overview of the benefits of assimilating TRMM and SSM/I rain rates and discuss developmental strategies for using space-based rainfall and rainfall-related observations to improve forecast models and climate datasets in preparation for the proposed multi-national Global Precipitation Mission (GPM).

Hou, Arthur↗

NASA NTRS2002

Summary of Cumulus Parameterization Workshop

A workshop on cumulus parameterization took place at the NASA Goddard Space Flight Center from December 3-5, 2001. The major objectives of this workshop were (1) to review the problem of representation of moist processes in large-scale models (mesoscale models, Numerical Weather Prediction models and Atmospheric General Circulation Models), (2) to review the state-of-the-art in cumulus parameterization schemes, and (3) to discuss the need for future research and applications. There were a total of 31 presentations and about 100 participants from the United States, Japan, the United Kingdom, France and South Korea. The specific presentations and discussions during the workshop are summarized in this paper.

Tao, Wei-Kuo↗

NASA NTRS2002

Impact of TRMM and SSM/I Rainfall Assimilation on Global Analysis and QPF

Evaluation of QPF skills requires quantitatively accurate precipitation analyses. We show that assimilation of surface rain rates derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and Special Sensor Microwave/Imager (SSM/I) improves quantitative precipitation estimates (QPE) and many aspects of global analyses. Short-range forecasts initialized with analyses with satellite rainfall data generally yield significantly higher QPF threat scores and better storm track predictions. These results were obtained using a variational procedure that minimizes the difference between the observed and model rain rates by correcting the moist physics tendency of the forecast model over a 6h assimilation window. In two case studies of Hurricanes Bonnie and Floyd, synoptic analysis shows that this procedure produces initial conditions with better-defined tropical storm features and stronger precipitation intensity associated with the storm.

Hou, Arthur↗

NASA NTRS2002

Better Weather Prediction and Climate Diagnostics Using Rainfall Measurements from Space

Progress in understanding of the role of water in global weather and climate is currently limited by our knowledge of the spatial and temporal variability of primary hydrological fields such as precipitation and evaporation. The Tropical Rainfall Measuring Mission (TRMM) has recently demonstrated that use of microwave-based rainfall observations from space in data assimilation can provide better climate data sets and improve short-range weather forecasting. At NASA, we have been exploring non-traditional approaches to assimilating TRMM Microwave Imager (TMI) and Special Sensor Microwavehager (SSM/I) surface rain rate and latent heating profile information in global systems. In this talk we show that assimilating microwave rain rates using a continuous variational assimilation scheme based on moisture tendency corrections improves quantitative precipitation estimates (QPE) and related clouds, radiation energy fluxes, and large-scale circulations in the Goddard Earth Observing System (GEOS) reanalyses. Short-range forecasts initialized with these improved analyses also yield better QPE scores and storm track predictions for Hurricanes Bonnie and Floyd. We present a status report on current efforts to assimilate convective and stratiform latent heating profile information within the general variational framework of model parameter estimation to seek further improvements. Within the next 5 years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007/2008. Based on what has been learned from TRMM, there is a high degree of confidence that these observations can play a'major role in improving weather forecasts and producing better global datasets for understanding the Earth's water and energy cycle. The key to success is to adopt an integrated approach to retrieval, validation, modeling, and data assimilation in a coordinated end-to-end observation-application program.

Hou, Arthur↗

NASA NTRS2001

The Impact of TRMM Data on Mesoscale Numerical Simulation of Super Typhoon Paka

This paper assesses the impact of TRMM Microwave Imager (TMI) derived surface rainfall data on the numerical simulation of Super Typhoon Paka (1997). A series of mesoscale numerical simulations is conducted for simulating Super Typhoon Paka during its mature stage by using the Penn State/NCAR MM5 model with initial conditions derived from the Goddard Earth Observing System (GEOS) global analyses with and without assimilation of the TMI surface rainfall data. Simulation results clearly demonstrate that the GEOS analysis with TMI rainfall data leads to a improved MM5 simulation of Typhoon Paka in terms of its intensity and kinematical and precipitation structures, since inclusion of the rainfall data into initial and boundary condition improves the storm environmental conditions (e.g., moisture and circulation). Further improvement can also be obtained by incorporating TMI data with mesoscale bogus vortex information into the initial conditions.

Pu, Zhao-Xia↗

NASA NTRS2001

The Impact of TRMM Data on Mesoscale Numerical Simulation of Super Typhoon Paka

Accurate measurement of the spatial and temporal variations of tropical rainfall around the globe had remained as a critical problem in meteorology until the recent launch of the Tropical Rainfall Measuring Mission (TRMM). TRMM offers a unique opportunity to improve understanding of tropical meteorology and also offers a great opportunity to evaluate the impact of rainfall data on tropical weather forecasts. This study assesses the impact of TRMM Microwave Imager (TMI) derived surface rainfall data on the numerical simulation of Super Typhoon Paka (1997).

Pu, Zhao-Xia↗

NASA NTRS2001

Use of Remotely-Sensed Rainfall Data in Global Modeling and Data Assimilation

Precipitation observations derived from microwave sensors available from the Tropical Rainfall Measuring Mission (TRMM) and the proposed Global Precipitation Mission (GPM) can provide crucial information needed for improving global modeling, data assimilation, and numerical weather prediction. New methodologies are being developed at NASA to make effective use of this new data type in these applications. Currently, global analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating 6-h averaged TRMM rainfall retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System. The improved analysis also leads to improved short-range forecasts in the tropics. The above results were obtained using a variational assimilation procedure that uses rainfall observations to derive moisture and temperature tendency corrections every 6 hours to compensate for errors arising from imperfect initial conditions and deficiencies in the model physics. We will describe a developmental path towards using space-borne rainfall data to empirically estimate and correct for state-dependent systematic errors in parameterized model physics. The study provides a demonstration of the potential of using remote-sensed rainfall data from microwave instruments to improve the 4-dimensional global datasets for climate analysis and numerical weather prediction.

Hou, Arthur↗

NASA NTRS2000

Does The Earth Have an Adaptive Infrared Iris?

Observations and analyses of water vapor and clouds in the tropics over the past decade suggest a different approach to radiative climate feedbacks: namely, that high clouds and high free-tropospheric relative humidity are largely tied to each other, and that the main feedback consists in changing the relative areas of cloudy/moist regions vis a vis clear/dry regions in response to the surface temperature of the cloudy/moist regions - as opposed to altering the humidity in either of the regions. This is an intrinsically 2-dimensional (horizontal and vertical) effect which does not readily enter simple 1-dimensional (vertical) radiative-convective schemes which emphasize average humidity, etc. Preliminary analyses of cloud data for the eastern part of the Western Pacific from the Japanese GMS-5(Geostationary Meteorological Satellite), are supportive of this suggestion - pointing to a 15% reduction in cloudy/moist area for a 1C increase of the sea surface temperature as measured by the cloud-weighted SST (sea surface temperature). The implication of this result is examined using a simple 2-dimensional radiative-convective model. The calculations show that such a change in the tropics would lead to a strong negative feedback in the global climate, with a feedback factor of about -1.7, which, if correct, would easily dominate the positive water vapor feedback found in current models. This new feedback mechanism, in effect, constitutes an adaptive infrared iris that opens and closes in order to control the OLR (outgoing longwave radiation) in response to changes in surface temperature in a manner similar to the way in which an eye's iris opens and closes in response to changing light levels. The climate sensitivity resulting from this thermostatic mechanism is consistent with the independent determination by Lindzen and Giannitisis (1998). Preliminary attempts to replicate observations with GCMs (General Circulation Models) suggest that models lack such a negative cloud/moist areal feedback.

Lindzen, Richard S.↗

NASA NTRS1999

Current and Future Plans of the NASA Data Assimilation Office (DAO)

The mission of the Data Assimilation Office (DAO) is to advance the state of the art of data assimilation and produce research-quality assimilated data sets which make optimal use of space-based observations. Development efforts over the last few years have focused on delivering a production data assimilation system in support of NASA's Terra platform. That system, called the Goddard Earth Observing System - version 2 or GEOS-2, represents a major upgrade to the baseline GEOS-1 system employed in NASA's first reanalysis effort. GEOS-2 includes a physical-space three dimensional variational analysis algorithm (the Physical-space Statistical Analysis System or PSAS) and numerous improvements to the general circulation model. The latter include a Soil-Vegetation-Atmosphere Transfer (SVAT) land surface scheme, a level 2.5 moist turbulence scheme and new Short Wave (SW) and Long Wave (LW) radiation code. The system also includes an off-line ozone assimilation system, and the capability to assimilate scatterometer surface winds, and TIROS Operational Vertical Sounder (TOVS) and Special Sensor Microwave Imager (SSM/I) moisture data. GEOS-2 is currently run at 1 degree horizontal resolution and 48 levels extending to O.Olmb. Experimental versions of GEOS-2 are run with a global stretched grid allowing enhanced (e.g. 1/4 deg) regional resolution. Other capabilities being developed include, the assimilation of Tropical Rainfall Measuring Mission (TRMM) precipitation and Global Positioning System (GPS) data, an off-line land surface assimilation system, and a retrospective analysis scheme. The DAO is also engaged in a number of collaborative efforts to help accelerate the development of the next generation data assimilation system. These include, a joint modeling effort between the DAO and NCAR/CGDD to develop a new Global Circulation Model (GCM), and a Department of Energy Lawrence Livermore National Laboratory (DOE/LLNL) collaboration on model parallelization. Plans for the next reanalysis will be discussed in the context of current and near term system quality and computing capabilities, and the need for multiple reanalysis products.

Atlas, Robert↗

NASA NTRS1999

The Tropical Rainfall Measuring Mission (TRMM) Progress Report

Recognizing the importance of rain in the tropics and the accompanying latent heat release, NASA for the U.S. and NASDA for Japan have partnered in the design, construction and flight of an Earth Probe satellite to measure tropical rainfall and calculate the associated heating. Primary mission goals are 1) the understanding of crucial links in climate variability by the hydrological cycle, 2) improvement in the large-scale models of weather and climate 3) Improvement in understanding cloud ensembles and their impacts on larger scale circulations. The linkage with the tropical oceans and landmasses are also emphasized. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in November 1997 with fuel enough to obtain a four to five year data set of rainfall over the global tropics from 37'N to 37'S. This paper reports progress from launch date through the spring of 1999. The data system and its products and their access is described, as are the algorithms used to obtain the data. Some exciting early results from TRMM are described. Some important algorithm improvements are shown. These will be used in the first total data reprocessing, scheduled to be complete in early 2000. The reader is given information on how to access and use the data.

Simpson, Joanne↗

NASA NTRS1999

The Tropical Rainfall Measuring Mission (TRMM)

Recognizing the importance of rain in the tropics and the accompanying latent heat release, NASA for the U.S. and NASDA for Japan have partnered in the design, construction and flight of an Earth Probe satellite to measure tropical rainfall and calculate the associated heating. Primary mission goals are: 1) the understanding of crucial links in climate variability by the hydrological cycle, 2) improvement in the large-scale models of weather and climate, and 3) improvement in understanding cloud ensembles and their impacts on larger scale circulations. The linkage with the tropical oceans and landmasses are also emphasized. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in November 1997 with fuel enough to obtain a four to five year data set of rainfall over the global tropics from 37 deg N to 37 deg S. This paper reports progress from launch date through the spring of 1999. The data system and its products and their access is described, as are the algorithms used to obtain the data. Some exciting early results from TRMM are described. Some important algorithm improvements are shown. These will be used in the first total data reprocessing, scheduled to be complete in early 2000. The reader is given information on how to access and use the data.

Simpson, Joanne↗