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Global Reference Atmospheric Model (GRAM) Suite Overview and Current Status

• Engineering-oriented atmospheric models that estimate mean values and statistical variations of atmospheric properties for numerous planetary destinations • Currently available for Earth, Mars, Venus, Titan, Neptune, Uranus, and Jupiter • Outputs include atmospheric density, temperature, pressure, chemical composition, radiative fluxes (for Mars-GRAM), and wind components along a user-defined path – Includes seasonal, diurnal, geographic, and altitude variations • Widely used by the engineering community because of their ability to create realistic atmospheric dispersions • Can be integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture • Optional trajectory input file consisting of time, height, latitude, and longitude can be used to provide the GRAM trajectory path • Optional auxiliary profile consisting of height, latitude, longitude, temperature, pressure, density, eastward wind, and northward wind may be used to replace model data in the GRAMs • Not a forecast model

atmospheric models↗
NASA NTRS2019

Global analysis and forecasts of carbon monoxide on Mars

Spatial and temporal variations in the Martian carbon monoxide (CO) cycle have been investigated through combining Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) retrievals of carbon monoxide mixing ratio and Mars Climate Sounder (MCS) temperature profiles with a Martian global circulation model (GCM) to produce the first global reanalysis of the Martian CO cycle. The reanalysis reduces the root mean square error between the forecast and CRISM CO retrievals by a factor of 2–4, dependent on the time of year. Forecasts initiated from the reanalysis show an improved match to standalone CRISM CO retrievals from northern winter solstice to northern summer solstice, indicating the benefit of data assimilation in simulating the CO abundance. The northern summer solstice CO minimum between 10°S – 50°S in the CRISM CO retrievals is found to be caused by a suppression of CO-enriched air leaking from the Hellas and Argyre basins, and is also strongly influenced by the amount of carbon dioxide condensing at the time. The GCM is the first reported to simulate the local CO minimum however it is weaker in strength, as CO-enriched air is also released northward in the area of Argyre basin in the GCM because of a shifted boundary between the southern polar vortex and mid-latitudes. The reanalysis, as a result of the assimilation of MCS temperature profiles, indicates that the polar vortex boundary is northward of Argyre basin and hence no northerly transport of CO-enriched air should be present over this location. Differences in local CO abundance between the reanalysis and GCM are also evident in spatio-temporal regions where no nearby CRISM CO retrievals are available as a result of alterations in local circulation and the sublimation/condensation of carbon dioxide through the combined assimilation of CRISM CO retrievals alongside MCS temperature profiles, illustrating how constraints can be imposed indirectly on the CO cycle through the powerful technique of data assimilation.

James A. Holmes↗
NASA NTRS1998

A Technique for Assimilating GOES-Derived Land Surface Products into Regional Models to Improve the Representation of Land Surface Forcing

As the parameterizations of surface energy budgets in regional models have become more complete physically, models have the potential to be much more realistic in simulations of coupling between surface radiation, hydrology, and surface energy transfer. Realizing the importance of properly specifying the surface energy budget, many institutions are using land-surface models to represent the lower boundary forcing associated with biophysical processes and soil hydrology. However, the added degrees of freedom due to inclusion of such land-surface schemes require the specification of additional parameters within the model system such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical distribution of soil moisture. A technique has been developed for assimilating GOES-IR skin temperature tendencies into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. An advantage of this technique for short-range forecasts is that it does not require a complex land-surface formulation within the atmospheric model. As a result, the need to specify poorly known soil and vegetative characteristics is eliminated. The GOES assimilation technique has been incorporated into the PSU/NCAR MM5. Results will be presented to demonstrate the ability of the assimilation scheme to improve short-term simulations of near-surface air temperature and mixing ratio during the warm season for several selected cases during June of 1995 and June 1997 which exhibit a variety of atmospheric and land-surface conditions. In addition, the simulations produced by the assimilation technique will be compared with those produced by the MM5/BATS coupled model initialized with soil water content derived from a soil hydrology model.

Lapenta, William M.↗
NASA NTRS1999

Data Assimilation Into a Coupled Ocean Atmosphere Model: Application to the 1997-1998 El Nino

As part of JPL's ocean data assimilation effort to study ocean circulation and seasonal-interannual climate variability, sea level anomaly observed by TOPEX altimeter, together with sea surface temperature and wind stress data, are assimilated into a simple coupled ocean atmosphere model of the tropical Pacific. Model-data consistency is examined. Impact of the assimilation (as initialization) on El Nino Southern Oscillation (ENSO) forecasts is evaluated. The coupled model consists of a shallow water component with two baroclinic modes, an Ekman shear layer, a simplified mixed-layer temperature equation, and a statistical atmosphere based on dominant correlations between historical surface temperature and wind stress anomaly data. The adjoins method is used to fit the coupled model to the data over various six-month periods from late 1996 to early 1998 by optimally adjusting the initial state, model parameters, and basis functions of the statistical atmosphere. On average, the coupled model can be fitted to the data to approximately within the data and representation errors (5 cm, 0.5 C, and 10 sq m/sq m for sea level, surface temperature, and pseudo wind stress anomalies, respectively). The estimated fields resemble observed spatio-temporal structure reasonably well. Hindcasts/forecasts of the 1997/1998 El Nino initialized from forced estimated ocean states and parameters are much more realistic than those simply initialized from ocean states (see figure below). In particular, the ability of the model to produce significant warming beyond the initial state is dramatically improved. Parameter estimation, which compensates for some model errors, is found to be important to obtaining better fits of the model to data and to improving forecasts.

Lee, Tong↗
NASA NTRS2001

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Kim, Kyu-Myong↗
NASA NTRS1982

Tropical cyclone track and genesis forecasting using satellite microwave sounder data

Although many dynamical and statistical prediction schemes are available to forecasters, tropical cyclone track errors are still large. One primary difficulty is that tropical cyclones exist over the data-sparse tropical oceans. Satellite sounders, however, routinely provide numerous data over these areas. Mean layer temperatures from the Scanning Microwave Spectrometer on board the Nimbus 6 satellite are decomposed using empirical orthogonal functions, and the expansion coefficients are related to deviations from the persistence forecast location, to speed change, to direction change and to intensity change. The significance of the regression equations is tested by a null hypothesis of zero correlation coefficient. It appears that significant information about tropical cyclone motion exists in the satellite-estimated mean layer temperatures, especially at upper levels. A physical interpretation of the statistical results is offered, and a one-storm-out independent test is used to test the stability of the equations. Finally, some further work is suggested.

Kidder, S. Q.↗
NASA NTRS1970

The integration of remote sensing data into global weather prediction, wave forecasting, and ocean circulation computer based systems

Data from infrared imaging systems and satellite infrared spectrometer (SIRS) for determining sea surface temperature and the atmospheric structure in cloudless areas over the oceans are discussed. Although some interpretations differ, it is clear that simultaneous measurements of radar sea return and passive microwave temperature will provide estimates of the wind speed, and perhaps wind direction, over the oceans, especially in cloudless areas, for a wide range of wind speeds. The problem of integrating the data that would be obtained by a spacecraft, especially one with a combination radar-radiometer, into global analysis procedures for meteorological, wave, and oceanographic predictions is described.

Pierson, W. J., Jr.↗
NASA NTRS2009

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should 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 an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

Zabodsky, Brad↗
NASA NTRS2012

Preliminary Results of a U.S. Deep South Warm Season Deep Convective Initiation Modeling Experiment using NASA SPoRT Initialization Datasets for Operational National Weather Service Local Model Runs

The initiation of deep convection during the warm season is a forecast challenge in the relative high instability and low wind shear environment of the U.S. Deep South. Despite improved knowledge of the character of well known mesoscale features such as local sea-, bay- and land-breezes, observations show the evolution of these features fall well short in fully describing the location of first initiates. A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA s Short-term Prediction Research and Transition (SPoRT) Center was undertaken during the 2012 warm season to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System. The NASA products were: a 4-km Land Information System data, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with a 9 km outer grid spacing and a 3 km inner nest spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the forecast timing and location of the first initiates, with a focus on the impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.

Medlin, Jeffrey M.↗
NASA NTRS2009

Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should 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 an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Chou, Shih-Hung↗
NASA NTRS2017

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about $200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa.

frost↗
NASA NTRS1998

Boreal Winter Predictions with the GEOS-2 GCM: The Role of Boundary Forcing and Initial Conditions

Ensembles of atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulations (1980-94) are analyzed to assess the controlling influences of boundary forcing and memory of the initial conditions. Both the forecasts and simulations are carried out with version 2 of the Goddard Earth Observing System (GEOS-2) GCM forced with observed sea surface temperatures (SSTs). While much of the focus is on the seasonal time scale (January- March) and the Pacific North American (PNA) region, we also present results for other regions, shorter time scales, and other known modes of variability in the northern hemisphere extratropics. Forecasts of indices of some of the key large-scale modes of variability show that there is considerable variability in skill between different regions of the Northern Hemisphere. The eastern North Atlantic region has the poorest long lead forecast skill showing no skill beyond about 10 days. Skillful seasonal forecasts are primarily confined to the wave-like ENSO response emanating from the tropical Pacific. In the Northern Hemisphere, this is associated with the well-known Pacific/North American (PNA) pattern. Memory of the initial conditions is the major factor leading to skillful extratropical forecasts of lead time less than one month, while SST forcing is the only factor at the seasonal time scale. SST forcing contributes to skillful forecasts at sub- seasonal time scales only over the PNA region. The GEOS-2 GCM produces average (1980-94) signal to noise ratios which are less than one everywhere in the extratropics, except for the subtropical Pacific where they approach 1.5. When confined to the ENSO years, the maximum signal to noise ratios occur in the PNA region where they exceed three. An assessment of the sampling distribution of the forecasts suggests the model's ENSO response is very likely too weak. These results show some sensitivity to the uncertainties in the estimates of the SST forcing fields. In the North Pacific region, the sensitivity to SST forcing manifests itself primarily as changes in the variability of the PNA response, underscoring the need for an ensemble approach to the seasonal prediction problem.

Chang, Yehui↗
NASA NTRS1977

Atmospheric model development in support of SEASAT. Volume 2: Analysis models

As part of the SEASAT program of NASA, two sets of analysis programs were developed for the Jet Propulsion Laboratory. One set of programs produce 63 x 63 horizontal mesh analyses on a polar stereographic grid. The other set produces 187 x 187 third mesh analyses. The parameters analyzed include sea surface temperature, sea level pressure and twelve levels of upper air temperature, height and wind analyses. The analysis output is used to initialize the primitive equation forecast models.

Langland, R. A.↗
NASA NTRS1982

The climatic effect of explosive volcanic activity: Analysis of the historical data

By using the most complete available records of direct beam radiation and volcanic eruptions, an historical analysis of the role of the latter in modulating the former was made. A very simple fallout and dispersion model was applied to the historical chronology of explosive eruptions. The resulting time series explains about 77 percent of the radiation variance, as well as suggests that tropical and subpolar eruptions are more important than mid-latitude eruptions in their impact on the stratospheric aerosol optical depth. The simpler climatic models indicate that past hemispheric temperature can be stimulated very well with volcanic and CO2 inputs and suggest that climate forecasting will also require volcano forecasting. There is some evidence that this is possible some years in advance.

Bryson, R. A.↗
NASA NTRS2005

Validation of Interannual Differences of AIRS Monthly Mean Parameters

Monthly mean fields of select geophysical parameters derived from analysis of AIRS/AMSU data, and their interannual differences, are shown and compared with analogous fields derived from other sources. All AIRS fields are derived using the AIRS Science Team Version 4 algorithm. Monthly mean results are shown for January 2004, as are interannual differences between January 2004 and January 2003. AIRS temperature and water vapor profile fields are compared with monthly mean collocated ECMWF 3 hour forecast and monthly mean TOVS Pathfinder Path A data. AIRS Tropospheric and Stratospheric coarse climate indicators are compared with analogous MSU products derived by Spencer and christy and found in the TOVS Pathfinder Path A data set. Total ozone is compared with results produced by TOMS. OLR is compared with OLR derived using CERES data and found in the TOVS Pathfinder Path A data set. AIRS results agree well in all cases, especially in the interannual difference sense.

Susskind, Joel↗
NASA NTRS2011

Evaluation of the Analysis Influence on Transport in Reanalysis Regional Water Cycles

Regional water cycles of reanalyses do not follow theoretical assumptions applicable to pure simulated budgets. The data analysis changes the wind, temperature and moisture, perturbing the theoretical balance. Of course, the analysis is correcting the model forecast error, so that the state fields should be more aligned with observations. Recently, it has been reported that the moisture convergence over continental regions, even those with significant quantities of radiosonde profiles present, can produce long term values not consistent with theoretical bounds. Specifically, long averages over continents produce some regions of moisture divergence. This implies that the observational analysis leads to a source of water in the region. One such region is the Unite States Great Plains, which many radiosonde and lidar wind observations are assimilated. We will utilize a new ancillary data set from the MERRA reanalysis called the Gridded Innovations and Observations (GIO) which provides the assimilated observations on MERRA's native grid allowing more thorough consideration of their impact on regional and global climatology. Included with the GIO data are the observation minus forecast (OmF) and observation minus analysis (OmA). Using OmF and OmA, we can identify the bias of the analysis against each observing system and gain a better understanding of the observations that are controlling the regional analysis. In this study we will focus on the wind and moisture assimilation.

Bosilovich, M. G.↗
NASA NTRS2010

Modelling the Future Hydroclimatology of the Lower Fraser River and its Impacts on the Spawning Migration Survival of Sockeye Salmon

Short episodic high temperature events can be lethal for migrating adult Pacific salmon (Oncorhynchus spp.). We downscaled temperatures for the Fraser River, British Columbia to evaluate the impact of climate warming on the frequency of exceeding thermal thresholds associated with salmon migratory success. Alarmingly, a modest 1.0 C increase in average summer water temperature over 100 years (1981-2000 to 2081-2100) tripled the number of days per year exceeding critical salmonid thermal thresholds (i.e. 19.0 C). Refined thresholds for two populations (Gates Creek and Weaver Creek) of sockeye salmon (Oncorhynchus nerka) were defined using physiological constraint models based on aerobic scope. While extreme temperatures leading to complete aerobic collapse remained unlikely under our warming scenario, both populations were increasingly forced to migrate upriver at reduced levels of aerobic performance (e.g. in 80% of future simulations, => 90% of salmon encountered temperatures exceeding population specific thermal optima for maximum aerobic scope; T(sub opt)) = 16.3 C for Gates Creek and T(sub sopt)=14.5 C for Weaver Creek). Assuming recent changes to river entry timing persist, we also predicted dramatic increases in the probability of freshwater mortality for Weaver Creek salmon due to reductions in aerobic, and general physiological, performance (e.g. in 42% of future simulations =>50% of Weaver Creek fish exceeded temperature thresholds associated with 0 - 60% of maximum aerobic scope). Potential for adaptation via directional selection on run-timing was more evident for the Weaver Creek population. Early entry Weaver Creek fish experienced 25% (range: 15 - 31%) more suboptimal temperatures than late entrants, compared with an 8% difference (range: 0 - 17%) between early and late Gates Creek fish. Our results emphasize the need to consider daily temperature variability in association with population-specific differences in behaviour and physiological constraints when forecasting impacts of climate change on migratory survival of aquatic species.

Hague, M. J.↗
NASA NTRS2018

Mt. Pinatubo's Impacts on the GEOS Forecasting System

The eruption of Mount Pinatubo in June 1991 had dramatic effects on the global climate, introducing a sudden and extreme forcing on the radiative budget. In this presentation we analyze the effects of the Mt. Pinatubo eruption on the seasonal forecast performed with Goddard Earth Observing System (GEOS), an Earth System Model that includes a bulk aerosol model coupled to radiation and an interactive ocean.We performed three ten-member ensembles of 12-month simulations (June 1991-May 1992). These simulations were performed with a 0.5 longitude by 0.5 latitude horizontal resolution. Out of the three ensembles, one excludes the eruption, representing the control experiment. The other two ensembles include the eruption of Mt. Pinatubo, varying in the effective radius of the volcanic sulfate: one assumed the effective radius to be equal to 0.35 micron (as for tropospheric aerosol) and the other has the effective radius set to 0.6 micron (closer to natural observation). Through the analysis of the aerosol forcing results derived from the two assumptions, we can show how this forcing acts on the seasonal forecast system. In particular, we will focus on the impacts to the surface and ocean temperatures and precipitation.

Mukherjee, Nikita↗