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NASA NTRS2009

The Principle of Energetic Consistency: Application to the Shallow-Water Equations

If the complete state of the earth's atmosphere (e.g., pressure, temperature, winds and humidity, everywhere throughout the atmosphere) were known at any particular initial time, then solving the equations that govern the dynamical behavior of the atmosphere would give the complete state at all subsequent times. Part of the difficulty of weather prediction is that the governing equations can only be solved approximately, which is what weather prediction models do. But weather forecasts would still be far from perfect even if the equations could be solved exactly, because the atmospheric state is not and cannot be known completely at any initial forecast time. Rather, the initial state for a weather forecast can only be estimated from incomplete observations taken near the initial time, through a process known as data assimilation. Weather prediction models carry out their computations on a grid of points covering the earth's atmosphere. The formulation of these models is guided by a mathematical convergence theory which guarantees that, given the exact initial state, the model solution approaches the exact solution of the governing equations as the computational grid is made more fine. For the data assimilation process, however, there does not yet exist a convergence theory. This book chapter represents an effort to begin establishing a convergence theory for data assimilation methods. The main result, which is called the principle of energetic consistency, provides a necessary condition that a convergent method must satisfy. Current methods violate this principle, as shown in earlier work of the author, and therefore are not convergent. The principle is illustrated by showing how to apply it as a simple test of convergence for proposed methods.

Cohn, Stephen E.↗
NASA NTRS2015

Intercomparison of Martian Lower Atmosphere Simulated Using Different Planetary Boundary Layer Parameterization Schemes

We use the mesoscale modeling capability of Mars Weather Research and Forecasting (MarsWRF) model to study the sensitivity of the simulated Martian lower atmosphere to differences in the parameterization of the planetary boundary layer (PBL). Characterization of the Martian atmosphere and realistic representation of processes such as mixing of tracers like dust depend on how well the model reproduces the evolution of the PBL structure. MarsWRF is based on the NCAR WRF model and it retains some of the PBL schemes available in the earth version. Published studies have examined the performance of different PBL schemes in NCAR WRF with the help of observations. Currently such assessments are not feasible for Martian atmospheric models due to lack of observations. It is of interest though to study the sensitivity of the model to PBL parameterization. Typically, for standard Martian atmospheric simulations, we have used the Medium Range Forecast (MRF) PBL scheme, which considers a correction term to the vertical gradients to incorporate nonlocal effects. For this study, we have also used two other parameterizations, a non-local closure scheme called Yonsei University (YSU) PBL scheme and a turbulent kinetic energy closure scheme called Mellor- Yamada-Janjic (MYJ) PBL scheme. We will present intercomparisons of the near surface temperature profiles, boundary layer heights, and wind obtained from the different simulations. We plan to use available temperature observations from Mini TES instrument onboard the rovers Spirit and Opportunity in evaluating the model results.

Natarajan, Murali↗
NASA NTRS1998

NASA's Earth Observing System: The Transition from Climate Monitoring to Climate Change Prediction

Earth's 4.5 billion year history is a study in change. Natural geological forces have been rearranging the surface features and climatic conditions of our planet since its beginning. There is scientific evidence that some of these natural changes have not only led to mass extinctions of species (e.g., dinosaurs), but have also severely impacted human civilizations. For instance, there is evidence that a relatively sudden climate change caused a 300-year drought that contributed to the downfall of Akkadia, one of the most powerful empires in the Middle-East region around 2200 BC. More recently, the "little ice age" from 1200-1400 AD forced the Vikings to abandon Greenland when temperatures there dropped by about 1.5 C, rendering it too difficult to grow enough crops to sustain the population. Today, there is compelling scientific evidence that human activities have attained the magnitude of a geological force and are speeding up the rate of global change. For example, carbon dioxide levels have risen 30 percent since the industrial revolution and about 40 percent of the world's land surface has been transformed by humans. We don't understand the cause-and-effect relationships among Earth's land, ocean, and atmosphere well enough to predict what, if any, impacts these rapid changes will have on future climate conditions. We need to make many measurements all over the world, over a long period of time, in order to assemble the information needed to construct accurate computer models that will enable us to forecast climate change. In 1988, the Earth System Sciences Committee, sponsored by NASA, issued a report calling for an integrated, long-term strategy for measuring the vital signs of Earth's climate system. The report urged that the measurements must all be intimately coupled with focused process studies, they must facilitate development of Earth system models, and they must be stored in an information system that ensures open access to consistent, long-term data. This committee emphasized that the only feasible way to collect these consistent, long-term data is through the use of space-based Earth "remote sensors" (instruments that can measure from a distance things like temperature).

King, Michael D.↗
NASA NTRS1978

Experiments in monthly mean simulation of the atmosphere with a coarse-mesh general circulation model

The Hansen atmospheric model was used to compute five monthly forecasts (October 1976 through February 1977). The comparison is based on an energetics analysis, meridional and vertical profiles, error statistics, and prognostic and observed mean maps. The monthly mean model simulations suffer from several defects. There is, in general, no skill in the simulation of the monthly mean sea-level pressure field, and only marginal skill is indicated for the 850 mb temperatures and 500 mb heights. The coarse-mesh model appears to generate a less satisfactory monthly mean simulation than the finer mesh GISS model.

Lutz, R. J.↗
NASA NTRS1984

Applications of VAS and TOVS to tropical cyclones

The results of the attempts to use VAS (visible IR spin scan radiometer atmospheric sounder) data for predicting tropical cyclones are reported. VAS, mounted on a GEO platform, has 14 detectors for upwelling radiance. The McIDAS software was, by the 1983 hurricane season, upgraded to providing imagery and mass, motion and moisture patterns using the satellite data, which also permitted determinations of accurate temperature and moisture profiles. Comparisons made between VAS and radiosonde data revealed fairly close agreement of satellite and in-situ conditions during the 1983 season. When combined with data from the Tiros operational vertical sounder (TOVS) to define wind streamlines and predict the path of a hurricane from data fed into numerical models, sufficiently good agreement was obtained to initiate plans to forecast a hurricane trajectory during the 1984 season.

Velden, C. S.↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2010

Influence of Satellite-Based Heterogeneous Vegetation Momentum Roughness on Mesoscale Model Dynamics During IHOP 2002

The sensitivity of mesoscale weather prediction model to a vegetation roughness initialization is investigated for the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database previously derived using a physically based procedure and MODIS imagery, and iii) a MODIS climatologic roughness database that possesses the same spatial heterogeneity as (i) but with mean land class values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations from meteorological stations within the Oklahoma mesonet and surrounding region during IHOP20O2. A sensitivity analysis on the impact the MODIS-based roughness fields is also made through a time-series intercomparison of temperature bias, probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE) the results that, for the current replacement of the standard land-cover type based roughness values with the satellite-derived fields statistically improves model performance for most of the observed variables. Further, the satellite-based roughness enhances the surface wind speed, PBL height and TKE production on the order of 3 to l0 percent, with a lesser effect over grassland and cropland domains, and the greater effect over mixed land cover domains

Jasinski, Michael↗
NASA NTRS1993

Analysis of the tropospheric water distribution during FIRE 2

The Penn State/NCAR mesoscale model, as adapted for use at ARC, was used as a testbed for the development and validation of cloud models for use in General Circulation Models (GCM's). This modeling approach also allows us to intercompare the predictions of the various cloud schemes within the same dynamical framework. The use of the PSU/NCAR mesoscale model also allows us to compare our results with FIRE-II (First International Satellite Cloud Climatology Project Regional Experiment) observations, instead of climate statistics. Though a promising approach, our work to date revealed several difficulties. First, the model by design is limited in spatial coverage and is only run for 12 to 48 hours at a time. Hence the quality of the simulation will depend heavily on the initial conditions. The poor quality of upper-tropospheric measurements of water vapor is well known and the situation is particularly bad for mid-latitude winter since the coupling with the surface is less direct than in summer so that relying on the model to spin-up a reasonable moisture field is not always successful. Though one of the most common atmospheric constituents, water vapor is relatively difficult to measure accurately, especially operationally over large areas. The standard NWS sondes have little sensitivity at the low temperatures where cirrus form and the data from the GOES 6.7 micron channel is difficult to quantify. For this reason, the goals of FIRE Cirrus II included characterizing the three-dimensional distribution of water vapor and clouds. In studying the data from FIRE Cirrus II, it was found that no single special observation technique provides accurate regional distributions of water vapor. The Raman lidar provides accurate measurements, but only at the Hub, for levels up to 10 km, and during nighttime hours. The CLASS sondes are more sensitive to moisture at low temperatures than are the NWS sondes, but the four stations only cover an area of two hundred kilometers on a side. The aircraft give the most accurate measurements of water vapor, but are limited in spatial and temporal coverage. This problem is partly alleviated by the use of the MAPS analyses, a four-dimensional data assimilation system that combines the previous 3-hour forecast with the available observations, but its upper-level moisture analyses are sometimes deficient because of the vapor measurement problem. An attempt was made to create a consistent four-dimensional description of the water vapor distribution during the second IFO by subjectively combining data from a variety of sources, including MAPS analyses, CLASS sondes, SPECTRE sondes, NWS sondes, GOES satellite analyses, radars, lidars, and microwave radiometers.

Westphal, Douglas L.↗
NASA NTRS2006

Comparison of Balloonsonde and Remote Sensing Atmospheric Measurements

As part of its aircraft icing research program, the NASA Glenn Research Center is conducting a program to develop technologies for the remote sensing of atmospheric conditions. A suite of instruments, currently ground-based, are used to identify a region of supercooled liquid water which is labeled as hazardous if its liquid water content is sufficiently high. During the recently completed Alliance Icing Research Study (AIRS II), these instruments were deployed in conjunction with those of other U.S. and Canadian researchers at the Mirabel Airport near Montreal. As part of the study, balloonsondes were employed to provide in-situ measurement of the atmospheric conditions that were being concurrently remotely sensed. Balloonsonde launches occurred daily at 1200 GMT to provide AIRS forecasters with local data and additionally when research aircraft were present in the airspace. In this paper, we compare the processed data from the NASA remote sensing instruments, which included an X-band radar, lidar and two radiometers, to the data gathered from the 70 soundings conducted while the NASA instruments were active. Among the parameters compared are cloud upper and lower boundaries, temperature and humidity profiles and freezing levels.

Brinker, David J.↗
NASA NTRS2007

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

Case, Jonathan L.↗
NASA NTRS2008

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Overview

This paper is an overview of the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) project, giving some history on the project, various applications of the atmospheric data, and future ideas and plans. As part of NASA's Aviation Safety and Security Program, the TAMDAR project developed a small low-cost sensor that collects useful meteorological data and makes them available in near real time to improve weather forecasts. This activity has been a joint effort with FAA, NOAA, universities, and industry. A tri-agency team collaborated by developing a concept of operations, determining the sensor specifications, and evaluating sensor performance as reported by Moosakhanian et. al. (2006). Under contract with Georgia Tech Research Institute, NASA worked with AirDat of Raleigh, NC to develop the sensor. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated and true air speed, ice accretion rate, wind speed and direction, peak and average turbulence, and eddy dissipation rate. The overall development process, sensor capabilities, and performance based on ground and flight tests is reported by Daniels (2002), Daniels et. al. (2004) and by Tsoucalas et. al. (2006). An in-service evaluation of the sensor was performed called the Great Lakes Fleet Experiment (GLFE), first reported by Moninger et. al. (2004) and Mamrosh et. al. (2005). In this experiment, a Mesaba Airlines fleet was equipped to collect meteorological data over the Great Lakes region during normal revenue-producing flights.

Daniels, Taumi S.↗
NASA NTRS2015

Evaluation of the 7-km GEOS-5 Nature Run

This report documents an evaluation by the Global Modeling and Assimilation Office (GMAO) of a two-year 7-km-resolution non-hydrostatic global mesoscale simulation produced with the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model. The simulation was produced as a Nature Run for conducting observing system simulation experiments (OSSEs). Generation of the GEOS-5 Nature Run (G5NR) was motivated in part by the desire of the OSSE community for an improved high-resolution sequel to an existing Nature Run produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has served the community for several years. The intended use of the G5NR in this context is for generating simulated observations to test proposed observing system designs regarding new instruments and their deployments. Because NASA's interest in OSSEs extends beyond traditional weather forecasting applications, the G5NR includes, in addition to standard meteorological components, a suite of aerosol types and several trace gas concentrations, with emissions downscaled to 10 km using ancillary information such as power plant location, population density and night-light information. The evaluation exercise described here involved more than twenty-five GMAO scientists investigating various aspects of the G5NR performance, including time mean temperature and wind fields, energy spectra, precipitation and the hydrological cycle, the representation of waves, tropical cyclones and midlatitude storms, land and ocean surface characteristics, the representation and forcing effects of clouds and radiation, dynamics of the stratosphere and mesosphere, and the representation of aerosols and trace gases. Comparisons are made with observational data sets when possible, as well as with reanalyses and other long model simulations. The evaluation is broad in scope, as it is meant to assess the overall realism of basic aspects of the G5NR deemed relevant to the conduct of OSSEs. However, because of the relatively short record and other practical considerations, these comparisons cannot provide a definitive, statistically sound assessment of all model deficiencies, or guarantee the G5NR's suitability for all OSSE applications. Differences between the observed and simulated behavior also must be judged in the context of basic internal atmospheric variability which can introduce variations that are not necessarily controlled by the prescribed sea surface temperatures used in generating the G5NR. The results show that the G5NR performs well as measured by the majority of metrics applied in this evaluation. Particular benefits derived from the 7-km resolution of G5NR include realistic representations of extreme weather events in both the tropics and extratropics including tropical cyclones, Nor'easters and mesoscale convective complexes; improved representation of the diurnal cycle of precipitation over land; well-resolved surface-atmosphere interactions such as katabatic wind flows over Antarctica and Greenland; and resolution of orographically generated gravity waves that propagate into the upper atmosphere and influence the large scale circulation. Obvious deficiencies in the G5NR include a "splitting" of the inter-tropical convergence zone, which leads to a weaker-than-observed Hadley circulation and related deficiencies in the depiction of stationary wave patterns. Also, while the G5NR captures global cloud features and radiative effects well in general, close comparison with observations reveals higher-than-observed cloud brightness, likely due to an overabundance of cloud condensate; less distinct cloud minima in subtropical subsidence zones, consistent with a weak Hadley circualtion; and too few near-coastal marine stratocumulus clouds.

GEOS-5↗
NASA NTRS2019

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and introduces uncertainty due to biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the Nature Run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared to estimates calculated using self-analysis verification. The Global Earth Observing System version 5 (GEOS-5) forecast model with Gridpoint Statistical Interpolation (GSI) is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small discrepancies in the adjoint model estimation of observation impact. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification, while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.

N C Prive↗
NASA NTRS2021

Understanding the Impacts of Land Surface and PBL Observations on the Terrestrial and Atmospheric Legs of Land-Atmosphere Coupling

Accurately representing land-atmosphere (L-A) interactions and coupling in NWP systems remains a challenge. New observations, incorporated into models via assimilation or calibration, hold the promise of improved forecast skill, but erroneous model coupling can hinder the benefits of such activities. To better understand model representation of coupled interactions and feedbacks, this study demonstrates a novel framework for coupled calibration of the Single Column Model (SCM) capability of the NASA Unified Weather Research and Forecasting (NU-WRF) system coupled to NASA’s Land Information System (LIS). The local land-atmosphere coupling (LoCo) process chain paradigm is used to assess the processes and connections revealed by calibration experiments. Two summer case studies in the U. S. Southern Great Plains are simulated in which LSM parameters are calibrated to diurnal observations of LoCo process chain components including 2-meter temperature, 2-meter humidity, surface fluxes (Bowen ratio), and PBL height. Results show a wide range of soil moisture and hydraulic parameter solutions depending on which L-A variable (i.e. observation) is used for calibration, highlighting that improvement in either SHP or ISM when not in tandem with the other can provide undesirable results. Overall, this work demonstrates that a process chain calibration approach can be used to assess L-A connections, feedbacks, strengths, and deficiencies in coupled models, as well as quantify the potential impact of new sources of observations of land-PBL variables on coupled prediction.

Patricia Lawston-Parker↗
NASA NTRS1986

Forecasting sea breeze thunderstorms at the Kennedy Space Center using the Prognostic Three-Dimensional Mesoscale Model (P 3DM)

A Prognostic Three-Dimensional Model (P3DM) to produce 1- to 12-hr predictions of sea breeze convective (SBC) storms at KSC is described. The P3DM was developed to account for a scale of about 10 km, interactions between surface heat and moisture fluxes, boundary layer convergence, the movement of moisture into cloud formation zones, and alterations in the convective potential in the lower levels of the atmosphere during the diurnal cycle. Initialized with wind, temperature, specific humidity and local water temperature data, the model allows for the distortion of the boundary layer moisture and thermal fields by sea breeze conditions. The results of three simulations of events leading to the onset of SBC storms are presented to demonstrate the model's capabilities, and techniques which may enhance the accuracy of the predictions are discussed.

Lyons, Walter A.↗
NASA NTRS1991

Hourly observations of the jet stream - Wind shear, Richardson number and pilot reports of turbulence

Results are presented of observations of the jet stream made on the basis of over 400 hr of wind and temperature data taken during two prolonged jet stream passages above western and central Pennsylvania during mid-November 1986 and mid-January 1987. Wind profilers are found to be far better suited for the detailed examination of jet stream structure than are weather balloons. The combination of good vertical resolution with not previously obtained temporal resolution reveals structural details not seen before. Development of probability forecasts of turbulence based on wind profiler-derived shear values appears possible. A good correlation between pilot reports and turbulence and wind shear is found.

Syrett, William J.↗
NASA NTRS2017

Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feed backs of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of above ground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to year variation in plant growth.

Zhang, Zhen↗
NASA NTRS2018

Near-Real Time Ocean-Atmosphere Skin Temperature as Part of NASA GMAO Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI).

SST↗