Recent technology advances in miniature microwave radiometers that can be hosted on very small satellites has made possible a new class of affordable constellation missions that provide very high revisit rates of tropical cyclones and other severe weather. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture–Instrument (EVI-3) program and is now in development with planned launch readiness in late 2019. The overarching goal for TROPICS is to provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones (TCs). TROPICS will provide rapid-refresh microwave measurements (median refresh rate better than 60 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. TROPICS will comprise a constellation of at least six CubeSats in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles and low-level moisture. This observing system offers an unprecedented combination of horizontal and temporal resolution in the microwave spectrum to measure environmental and inner-core conditions for TCs on a nearly global scale and is a major leap forward in the temporal resolution of several key parameters needed for assimilation into advanced data assimilation systems capable of utilizing rapid-update radiance or retrieval data. Here, we provide an overview of the mission and an update on current status, with a focus on unique characteristics of the Cubesat system, recent performance simulations on a range of observables to be provided by the constellation, and a summary of science applications.
The objective of this research is to evaluate the potential for CubeSats and other small satellite constellations to mitigate a potential data gap and/or improve numerical weather forecasts. This is conducted as a collaborative project involving NOAA AOML and NESDIS/STAR, CIMSS, and the University of MD. To this end, a series of Observing System Simulation Experiments (OSSEs) is being conducted. These experiments utilize both global and regional OSSE systems. Specific instruments being evaluated include the CubeSat Infrared Atmospheric Sounder (CIRAS), the Micro-sized Microwave Atmospheric Sounder-2 (MicroMAS-2), the Cyclone Global Navigation Satellite System (CYGNSS), and the Time-Resolved Observations of Precipitation and storm Intensity with a Constellation of Smallsats (TROPICS) mission. The diverse set of OSSEs that have been performed to date show potential for each of these systems to mitigate a data gap and to contribute to improved prediction and begin to quantify their relative impacts.
One of the most important applications of a space-based Doppler Wind Lidar (DWL) would be to improve atmospheric analyses and weather forecasting. Since the mid-1980s, Observing System Simulation Experiments (OSSEs) have been conducted to evaluate the potential impact of space-based DWL data on numerical weather prediction (NWP). All of these OSSEs have shown significant beneficial impact on global analyses and forecasts. In more recent years, a limited number of experiments have been conducted to evaluate the potential impact of DWL data on hurricane forecasting and also to begin to evaluate the impact of real airborne DWL observations. These latest studies suggest that DWL can complement existing hurricane observations effectively and have the potential to contribute to improved hurricane track and intensity forecasting.
Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at NASA/ GSFC and NOAA/AOML over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize early applications of global OSSEs to hurricane track forecasting and new experiments using both global and regional models. These experiments are aimed at determining (1) the potential impact of unmanned aerial systems, (2) the relative impact of alternative concepts for space-based lidar winds, and (3) the relative impact of alternative concepts for polar and geostationary hyperspectral sounders.
KEYWORDS: LIDAR, Data modeling, Satellites, Atmospheric modeling, Meteorological satellites, Systems modeling, 3D modeling, Computer simulations, Environmental sensing, Data centers
Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Detailed OSSEs have been conducted at NASA/ GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize early applications of global OSSEs to hurricane track forecasting and new experiments, using both global and regional models, that are aimed at both track and intensity forecasting.
KEYWORDS: LIDAR, Data modeling, Atmospheric modeling, Satellites, Meteorological satellites, Systems modeling, Computer simulations, 3D modeling, Environmental sensing, Data centers
Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of
proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and
assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at NASA/
GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers
over the last 27 years. These OSSEs determined correctly the quantitative potential for several proposed satellite
observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits,
coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the
first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize
applications of global OSSEs to hurricane track forecasting, and current experiments using both global and regional
models aimed at both track and intensity forecasting.
Since the 1970s, an extensive series of data impact studies has been performed to evaluate and enhance the impact of
satellite surface wind data on ocean surface wind analyses and fluxes, atmospheric and oceanic modeling, and weather
prediction. These studies led to the first beneficial impacts of scatterometer winds on numerical weather prediction
(NWP), the development of the methodology to assimilate surface wind speeds derived from passive microwave
radiometry, and the operational use of satellite surface winds by marine forecasters and NWP models. In recent years,
the impact of these data on NWP has decreased as more competing data have become available; however, the results of
our recent experiments still show a very significant impact of satellite surface winds on ocean surface wind analyses and
on the prediction of selected storms over the oceans.
KEYWORDS: Satellites, Microwave radiation, Climatology, Analytical research, Received signal strength, Data modeling, Atmospheric modeling, Data centers, Meteorology, Spatial resolution
A cross-calibrated, multi-satellite ocean surface wind data is described. It covers the global ocean for the twenty-one
year period from 1987 to 2008 with 6-hour and 25-km resolution. This data set is produced using all ocean surface wind
speed observations from SSM/I, AMSR-E, and TMI, and all ocean surface wind vector observations from QuikSCAT
and SeaWinds. An enhanced variational analysis method (VAM) performs quality control and combines these data with
available conventional ship and buoy data and ECMWF analyses. The VAM analyses fit the data used and withheld data
very closely and contain small-scale structures not present in operational analyses. These data should be extremely useful
to atmospheric and oceanic research, and to air-sea interaction studies.
A new set of cross-calibrated, multi-satellite ocean surface wind data is described. The principal data set covers the
global ocean for the period beginning in 1987 with six-hour and 25-km resolution, and is produced by combining all
ocean surface wind speed observations from SSM/I, AMSR-E, and TMI, and all ocean surface wind vector observations
from QuikSCAT and SeaWinds. An enhanced variational analysis method (VAM) performs quality control and
combines these data with available conventional ship and buoy data and ECMWF analyses. The VAM analyses fit the
data used very closely and contain small-scale structures not present in operational analyses. Comparisons with withheld
WindSat observations are also shown to be very good. These data sets should be extremely useful to atmospheric and
oceanic research, and to air-sea interaction studies.
Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of
proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and
assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at NASA/
GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers
over the last 23 years. These OSSEs determined correctly the quantitative potential for several proposed satellite
observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits,
coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the
first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize OSSE
methodology and earlier OSSE results, and present methodology and results from recent OSSEs.
Global measurement of tropospheric winds is a key
measurement for understanding atmospheric
dynamics and improving numerical weather
prediction. Global wind profiles remain a high
priority for the operational weather community and
also for a variety of research applications including
studies of the global hydrologic cycle and transport
studies of aerosols and trace species. In addition to
space based winds, high altitude airborne Doppler
lidar systems flown on research aircraft, UAV's or
other advanced sub-orbital platforms would be of
great scientific benefit for studying mesoscale
dynamics and storm systems such as hurricanes. The
Tropospheric Wind Lidar Technology Experiment
(TWiLiTE) is a three year program to advance the
technology readiness level of the key technologies and
subsystems of a molecular direct detection wind lidar
system by validating them, at the system level, in an
integrated airborne lidar system. The TWiLiTE
Doppler lidar system is designed for autonomous
operation on the WB57, a high altitude aircraft
operated by NASA Johnson. The WB57 is capable of
flying well above the mid-latitude tropopause so the
downward looking lidar will measure complete
profiles of the horizontal wind field through the
lower stratosphere and the entire troposphere. The
completed system will have the capability to profile
winds in clear air from the aircraft altitude of 18 km
to the surface with 250 m vertical resolution and < 3
m/s velocity accuracy. Progress in technology
development and status of the instrument design will
be presented.
A detailed evaluation of the latest version of WINDSAT surface wind data has recently been performed to determine the quality of these data and their usefulness for ocean surface wind analysis and numerical weather prediction. The first component of this evaluation consisted of both subjective and objective comparisons of WINDSAT wind vectors to other sources of ocean surface winds (eg. ship and buoy observations, Quikscat satellite winds, or model derived wind analyses). This was followed by data impact experiments using a variational surface wind analysis, as well as an operational four-dimensional data assimilation system. The results of this evaluation demonstrate the usefulness of WINDSAT data, but also show deficiencies relative to current scatterometer measurements.
The assimilation of remotely sensed data from aircraft and satellites has contributed substantially to the current accuracy of operational hurricane forecasting. In the 1960's, satellite imagery revolutionized hurricane detection and forecasting. Since that time, quantitative remotely sensed data (eg. atmospheric motion winds, passive infrared and microwave radiances or retrievals of temperature, moisture, surface wind and rain rate, active microwave measurements of surface wind and rain rate) and significant advances in modeling and data assimilation have increased the accuracy of hurricane track forecasts very significantly. The development of advanced next-generation models in combination new types of remotely sensed observations (eg. space-based lidar winds) should yield significant further improvements in the timing and location of landfall and in the predicted intensification of hurricanes.
Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. OSSEs conducted at NASA GSFC and elsewhere have indicated significant potential for space-based lidar winds to improve numerical weather prediction. In this paper we summarize OSSE methodology and earlier OSSE results, and present methodology and new results from a Quick OSSE designed to assess the potential impact of lidar winds on the predicted track of a specific hurricane.
KEYWORDS: Atmospheric modeling, Data modeling, Satellites, Control systems, Meteorological satellites, Data centers, 3D modeling, Computer simulations, Systems modeling, Clouds
Observing system simulation experiments (OSSE) conducted prior to the launch of AIRS indicated significant potential for AIRS temperature soundings to improve numerical weather prediction (NWP), provided that cloud effects could be cleared effectively. Since the launch of AIRS aboard the AQUA satellite, a detailed geophysical validation of AIRS data has been performed. This included collocations of AIRS temperatures with in situ observations and model analyses, and observing system experiments (OSEs) to evaluate the actual impact of AIRS data on NWP. At the NASA Goddard Space Flight Center, we are evaluating AIRS data in several different forms, and are performing impact studies using multiple data assimilation systems. In general, the results of the OSE confirm the results of the earlier simulation experiments in that a meaningful positive impact of AIRS data is obtained and this impact depends strongly upon the assimilation of partially cloudy AIRS data.
Simultaneous use of AIRS/AMSU-A observations allow for the determination of accurate atmospheric soundings under partial cloud cover conditions. The methodology involves the determination of the radiances AIRS would have seen if the AIRS fields of view were clear, called clear column radiances, and use of these radiances to infer the atmospheric and surface conditions giving rise to these clear column radiances. Susskind et al. demonstrate via simulation that accurate temperature soundings and clear column radiances can be derived from AIRS/AMSU-A observations in cases of up to 80% partial cloud cover, with only a small degradation in accuracy compared to that obtained in clear scenes. Susskind and Atlas show that these findings hold for real AIRS/AMSU-A soundings as well. For data assimilation purposes, this small degradation in accuracy is more than offset by a significant increase in spatial coverage (roughly 50% of global cases were accepted, compared to 3.6% of the global cases being diagnosed as clear), and assimilation of AIRS temperature soundings in partially cloudy conditions resulted in a larger improvement in forecast skill than when AIRS soundings were assimilated only under clear conditions. Alternatively, derived AIRS clear column radiances under partial cloud cover could also be used for data assimilation purposes. Further improvements in AIRS sounding methodology have been made since the results shown in Susskind and Atlas. A new version of the AIRS/AMSU-A retrieval algorithm, Version 4.0, was delivered to the Goddard DAAC in February 2005 for production of AIRS derived products, including clear column radiances. The major improvement in the Version 4.0 retrieval algorithm is with regard to a more flexible, parameter dependent, quality control. Results are shown of the accuracy and spatial distribution of temperature-moisture profiles and clear column radiances derived from AIRS/AMSU-A as a function of fractional cloud cover using the Version 4.0 algorithm. Use of the Version 4.0 AIRS temperature profiles increased the positive forecast impact arising from AIRS retrievals relative to what was shown in Susskind and Atlas.
Satellite observations are a critical component of the global atmospheric observing system, and contribute substantially to the current accuracy of numerical weather forecasts. In this paper, two types of experiments related to the effectiveness of these and other observations are described. These are: Observing System Experiments (OSEs), which are conducted to evaluate the impact of an existing observing system; and Observing System Simulation Experiments (OSSEs) which are conducted to evaluate the potential for future observing systems to improve NWP, as well as to evaluate trade-offs in observing system design, and to develop and test improved methods for data assimilation. This paper summarizes the methodology for such experiments and presents selected results from OSEs to evaluate satellite data sets that have recently become available to the global observing system, such as AIRS and SeaWinds, and results from recent OSSEs to determine the potential impact of space-based lidar winds.
One of the important applications of satellite surface wind observations is to increase the accuracy of weather analyses and forecasts. The first satellite to measure surface wind over the oceans was Seasat in 1978. On board was a scatterometer, which measured radar backscatter from centimeter-scale capillary waves, from which surface wind speed and direction could be deduced. In more recent years, passive microwave remote sensing of the ocean surface has provided extensive observations of surface wind speed, and advanced scatterometers have been providing surface wind velocity data over the oceans. The initial impact of satellite surface wind data on weather analysis and forecasting was very small, but extensive research has been conducted since the early days of Seasat to improve the data accuracy and the utilization of these data in atmospheric models. Current satellite surface wind data are used to improve the detection of intense storms over the ocean, as well as to improve the overall representation of the wind field in numerical weather prediction models. As a result, these data are contributing to improved warnings for ships at sea and to improved global weather forecasts. Recent experiments conducted with data from the SeaWinds scatterometers aboard both Quikscat and ADEOS 2 indicate that increased coverage of scatterometer data can lead to even larger impacts than are routinely obtained now.
AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU/HSB are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and 1 km tropospheric layer precipitable water with an rms error of 20%, in cases with up to 80% effective cloud cover. Pre-launch simulation studies indicated that these results should be achievable. Minor modifications have been made to the pre-launch retrieval algorithm as alluded to in this paper. Sample fields of parameters retrieved from AIRS/AMSU/HSB data are presented and temperature profiles are validated as a function of retrieved effective fractional cloud cover. As in simulation, the degradation of retrieval accuracy with increasing cloud cover is small. Select fields are also compared to those contained in the ECMWF analysis, done without the benefit of AIRS data, to demonstrate information that AIRS can add to that already contained in the ECMWF analysis. Assimilation of AIRS temperature soundings in up to 80% cloud cover for the month of January 2003 into the GSFC FVSSI data assimilation system resulted in improved 5 day forecasts globally, both with regard to anomaly correlation coefficients and the prediction of location and intensity of cyclones.
Observing system simulation experiments (OSSE's) provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure realism of the OSSE's, and in the interpretation of OSSE results. All of the OSSE's that have been conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for different data assimilation systems, analysis methodology, and model resolutions. OSSE's clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone.
The SeaWinds scatterometer (like NSCAT and ERS) is able to detect unequivocal signatures of meteorological features including cyclones, fronts, anticyclones, easterly waves and other precursors of hurricanes and typhoons. Through collaborative efforts between NASA and NOAA, National Weather Service marine forecasters are using SeaWinds data to improve analyses, forecasts and significant weather warnings for maritime interests. This results in substantial economic savings as well as the reduction of weather related loss of life at sea. The impact of SeaWinds on Numerical Weather Prediction models is on average modest but occasionally results in significant forecast improvements.
Observing system simulation experiments (OSSE's) provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure realism of the OSSE's, and in the interpretation of OSSE results. All of the OSSE's that have been conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for differing data assimilation systems, analysis methodology, and model resolutions. OSSE's clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone.
Through the use of observation operators, modern data assimilation systems can ingest observations of quantities that are not themselves model variables, but are mathematically related to those variables. An example of this are the LOS (line of sight) winds that Doppler wind lidars provide. The model - or data assimilation system - needs information about both components of the horizontal wind vectors, whereas the individual observations in this case only provide the projection of the wind vector onto a given direction. In order to assess the expected impact of such an observing system, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial and angular distributions as well as the observation error characteristics. The basic design of the system will be presented along with experimental results obtained with it. In particular, the value of measuring LOS winds along two different directions for a given location will be discussed.
KEYWORDS: LIDAR, Satellites, Atmospheric modeling, Error analysis, Data modeling, 3D modeling, Analytical research, Systems modeling, Data centers, Earth observing sensors
One of the major applications of space-based Doppler wind lidar is to improve atmospheric analyses and numerical weather prediction (NWP). Since the mid 1980s, Observing System Simulation Experiments (OSSEs) have been conducted in order to evaluate the potential impact of lidar winds on NWP. These experiments have shown tremendous potential for satellite lidar observations to improve atmospheric analyses and forecasts. In addition, the OSSEs are providing an evaluation of trade-offs in lidar design, and are currently being used to define the specific requirements for lidar winds in terms of horizontal and vertical coverage and accuracy.
Global wind profiles are needed for a wide range of meteorological applications. Since the 1980's, observing system simulation experiments have been conducted in order to evaluate the potential impact of space-based wind profiler data on numerical weather prediction, and to evaluate trade-offs in lidar design. These experiments indicated tremendous potential for satellite lidar observations to improve atmospheric analyses and forecast. More recent experiments are aimed at assessing the precise requirements for space-based lidar wind profile data and to evaluate the potential for alternative technologies.
A detailed evaluation of NASA scatterometer (NSCAT) data has recently been performed to determine the error characteristics of this data type and its applicability to ocean surface analysis and numerical weather prediction. The first component of this evaluation consisted of both subjective and objective comparisons of NSCAT winds to ship and buoy observations, Goddard EOS (GEOS) and National Centers for Environmental Prediction (NCEP) wind analyses, European Space Agency European Remote Sensing Satellite wind vectors, and Special Sensor Microwave Imager wind speeds. This was then followed by a series of data assimilation and forecast experiments using the GEOS-1 data assimilation system (DAS), the prototype for the GEOS-2 DAS, and an earlier version of the NCEP DAS, that was operational in 1995. This paper will provide a brief summary of these experiments and a few illustrations of the results obtained.
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