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NASA NTRS2009

Severe Weather and Weak Waterspout Checklist in MIDDS

The goal of this task was to migrate the functionality of the AMU web-based Severe Weather Forecast Decision Aid and the 45 WS Weak Waterspout Checklist to MIDDS, the operational data ingest and display system of the 45 WS. Forecasting the occurrence and timing of warm season severe weather and weak waterspouts is challenging for 45 WS operational personnel. These interactive tools assist forecasters in determining the probability of issuing severe weather watches and warnings for the day. MIDDS is able retrieve many of the needed parameter values for the worksheet automatically. The AMU was able to develop user-friendly tools in MIDDS for both of these tools using McBASI coded programs. The tools retrieve needed values from MIDDS automatically, and require the forecaster to answer a few subjective questions. Both tools were tested and previewed to the 45 WS on MIDDS. In their previous forms, the forecasters enter values into both tools manually to output a threat index. Making these tools more automatic will reduce the possibility of human error and increase efficiency.

Wheeler, Mark M.↗
NASA NTRS2005

Assimilation of MLS and OMI Ozone Data

Ozone data from Aura Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) were assimilated into the ozone model at NASA's Global Modeling and Assimilation Office (GMAO). This assimilation produces ozone fields that are superior to those from the operational GMAO assimilation of Solar Backscatter Ultraviolet (SBUV/2) instrument data. Assimilation of Aura data improves the representation of the "ozone hole" and the agreement with independent Stratospheric Aerosol and Gas Experiment (SAGE) III and ozone sonde data. Ozone in the lower stratosphere is captured better: mean state, vertical gradients, spatial and temporal variability are all improved. Inclusion of OMI and MLS data together, or separately, in the assimilation system provides a way of checking how consistent OMI and MLS data are with each other, and with the ozone model. We found that differences between OMI total ozone column data and model forecasts decrease after MLS data are assimilated. This indicates that MLS stratospheric ozone profiles are consistent with OMI total ozone columns. The evaluation of error characteristics of OMI and MLS ozone will continue as data from newer versions of retrievals becomes available. We report on the initial step in obtaining global assimilated ozone fields that combine measurements from different Aura instruments, the ozone model at the GMAO, and their respective error characteristics. We plan to use assimilated ozone fields in estimation of tropospheric ozone. We also plan to investigate impacts of assimilated ozone fields on numerical weather prediction through their use in radiative models and in the assimilation of infrared nadir radiance data from NASA's Advanced Infrared Sounder (AIRS).

Stajner, I.↗
NASA NTRS2007

Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.

Susskind, Joel↗
NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and 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 in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, S.-H.↗
NASA NTRS2015

The Impact of Assimilating Precipitation-affected Radiance on Cloud and Precipitation in Goddard WRF-EDAS Analyses

High-frequency TMI and AMSR-E radiances, which are sensitive to precipitation over land, are assimilated into the Goddard Weather Research and Forecasting Model- Ensemble Data Assimilation System (WRF-EDAS) for a few heavy rain events over the continental US. Independent observations from surface rainfall, satellite IR brightness temperatures, as well as ground-radar reflectivity profiles are used to evaluate the impact of assimilating rain-sensitive radiances on cloud and precipitation within WRF-EDAS. The evaluations go beyond comparisons of forecast skills and domain-mean statistics, and focus on studying the cloud and precipitation features in the jointed rainradiance and rain-cloud space, with particular attentions on vertical distributions of height-dependent cloud types and collective effect of cloud hydrometers. Such a methodology is very helpful to understand limitations and sources of errors in rainaffected radiance assimilations. It is found that the assimilation of rain-sensitive radiances can reduce the mismatch between model analyses and observations by reasonably enhancing/reducing convective intensity over areas where the observation indicates precipitation, and suppressing convection over areas where the model forecast indicates rain but the observation does not. It is also noted that instead of generating sufficient low-level warmrain clouds as in observations, the model analysis tends to produce many spurious upperlevel clouds containing small amount of ice water content. This discrepancy is associated with insufficient information in ice-water-sensitive radiances to address the vertical distribution of clouds with small amount of ice water content. Such a problem will likely be mitigated when multi-channel multi-frequency radiances/reflectivity are assimilated over land along with sufficiently accurate surface emissivity information to better constrain the vertical distribution of cloud hydrometers.

WRF-EDAS Analyses↗
NASA NTRS2012

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

Geomagnetic Storms↗
NASA NTRS2021

A Phenomenon-Based Decomposition of Model-Based Estimates of Boreal Winter ENSO Variability

Climate models are now routinely being used to simulate and predict climate variability on time scales ranging from sub-seasonal to seasonal and longer. As such, there are now long histories of such simulations and predictions spanning multiple decades and multiple ensemble members, both of which are crucial for separating climate signal from climate noise. A key focus of such runs has been the El Niño-Southern Oscillation (ENSO), spurred by recent improvements in our ability to predict such events, though questions remain as to how well climate models do beyond simply always predicting the “canonical” atmospheric response to an ENSO event—something simple statistical models already do reasonably well. This is a critical issue that needs addressing, given the importance of event-to-event differences for predicting regional impacts of ENSO teleconnections, and the need to justify the expense of running sophisticated climate models. Unfortunately, current diagnostic tools are not well suited for quantifying the different sources of variability associated with specific phenomena such as ENSO. More generally, while much effort has focused on addressing model bias, less has been done to address errors in second moment statistics—an issue whose importance is gaining increased attention particularly as we build climate prediction systems capable of taking advantage of forecasts of opportunity—a capability that requires reliable estimates of forecast uncertainty. In this report, we outline a phenomenon-based statistical decomposition of climate variance(in essence a detailed variance budget)that is specifically tailored to address the above questions by separating the variability (both the signal and noise) into that tied to the long-term average impact of a particular phenomenon(the composite mean) and the event-to-event(E2E) variability about the composite mean. In addition, we provide related decompositions of the correlations that allow us to quantify how much of the agreement with observations (the skill) comes from the composite mean as opposed to from the E2Evariability. As an example, we present the results of such a decomposition for ENSO based on simulations with the GEOS atmospheric general circulation model (AGCM), with a focus on the monthly mean impacts over North America during boreal winter(December –March). Here we take advantage of existing GEOS AGCM simulations that were produced as companion simulations to MERRA-2for the period 1980-2016. Comparisons are made throughout with MERRA-2.

ENSO↗
NASA NTRS1999

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

Rokke, L.↗
NASA NTRS1991

An assessment of VAS-derived retrievals and parameters used in thunderstorm forecasting

VAS satellite retrievals and derived parameters used in forecasting severe local storms were evaluated against corresponding radiosonde values. VAS products were also compared with the first-guess input to the retrieval algorithm. The evaluation methodology was to pair each radiosonde observation with the closest VAS retrieval within 50 km during a four-month period in 1986. VAS temperatures agreed closely with radiosonde values; dewpoints showed less agreement. Sounding differences were poorly correlated with the number of pixels from which the retrievals were prepared. VAS discrepancies usually were not well correlated with first-guess errors. Horizontal gradients of VAS products were stronger than those from radiosondes. VAS precipitable water agreed better with ground truth than did dewpoints at individual levels, but VAS thicknesses were not much improved. Results for severe storms forecasting parameters indicated that discrepancies increased with the amount of manipulation required during computation. The lifted index showed the best agreement; positive bouyant energy showed poor comparisons.

Fuelberg, Henry E.↗
NASA NTRS2002

Impact of Mineral Aerosol on TOVS Temperature and Moisture Retrievals

Mineral aerosols can absorb significant radiation in the infrared spectrum. Consequently, there may be errors in TIROS Operational Vertical Sounder (TOVS) retrieved temperature and moisture profiles in regions of heavy dust loading. We first investigate the potential error in the temperature retrievals and secondly attempt to account for radiative effects of the dust in retrievals. Information on the dust concentrations and size distribution is from the Goddard Chemistry Aerosol Transport model (GOCART). Aerosol optical parameters are calculated from mie scattering theory assuming a composition of pure illite. We used the cloud-clearing DAO TOVS retrieval system of Joiner and Rokke (2000). It is incorporated into the Data Assimilation Office (DAO) Finite Volume Data Assimilation System (NDAS). The advantage of this approach is that the first guess temperature profile used in the TOVS retrieval are forecasted temperatures from the previous assimilated time period. The operational DAO fvDAS was run for 10 days during June 2001 during a period of dust outbreaks off the coast of Africa over the Atlantic. The observed minus the forecast (O-F) brightness temperature at each TOVS channel is a measure of the accuracy of the retrieval. Since there was no account of dust during this operational run, a dependence of O-F on the estimated atmospheric dust concentrations from GOCART indicates that the dust is contaminating the TOVS retrievals. Channels that measure the surface temperature, lower tropospheric temperature and moisture show this dependence. There are errors in the retrieved brightness temperature of a half a degree or more during heavy dust loading conditions. The forecasted brightness temperature is always greater than the observed value. The radiative transfer module used in the DAO TOVS retrieval system was modified to account for dust. We calculate the sensitivity of the brightness temperature of the TOVS channels to the dust concentrations in GOCART assuming pure illite. For most channels the observed relationship between O-F and dust concentrations in GOCART is consistent with these calculated sensitivities. The fvDAS run was repeated using the modified DAO TOVS retrieval system that accounts for dust. Preliminary results from this run show that there are significant effects on the retrieved surface temperature and tropospheric moisture.

Weaver, Clark↗
NASA NTRS2019

The WRF Lightning Forecasting Algorithm: Sensitivities to Microphysics and Boundary Layer Physics Schemes

As with WRF model simulated convection in general, LFA output is sensitive to cloud and PBL physics. WRF convection is also sensitive to model initialization uncertainties and unresolved errors.  Large sensitivity exists in HRRR-like Thompson microphysics and PBL, for which original LFA calibration constant needs to be multiplied by 1.13 to give proper FRD amplitudes. Thompson microphysics scheme also shows poorest correlations relative to reference data, suggesting low predictability of HRRR LFA output. Need to validate HRRR LFA against GLM observations.

weather prediction↗
NASA NTRS2010

Use of Quality Controlled AIRS Temperature Soundings to Improve Forecast Skill

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-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. Also included are the clear column radiances used to derive these products which are representative of the radiances AIRS would have seen if there were no clouds in the field of view. All products also have error estimates. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and layer precipitable water with an rms error of 20 percent, in cases with up to 90 percent effective cloud cover. The products are designed for data assimilation purposes for the improvement of numerical weather prediction, as well as for the study of climate and meteorological processes. With regard to data assimilation, one can use either the products themselves or the clear column radiances from which the products were derived. The AIRS Version 5 retrieval algorithm is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates for retrieved quantities and clear column radiances, and the use of these error estimates for Quality Control. The temperature profile error estimates are used to determine a case-by-case characteristic pressure pbest, down to which the profile is considered acceptable for data assimilation purposes. The characteristic pressure p(sub best) is determined by comparing the case dependent error estimate (delta)T(p) to the threshold values (Delta)T(p). The AIRS Version 5 data set provides error estimates of T(p) at all levels, and also profile dependent values of pbest based on use of a Standard profile dependent threshold (Delta)T(p). These Standard thresholds were designed as a compromise between optimal use for data assimilation purposes, which requires highest accuracy (tighter Quality Control), and climate purposes, which requires more spatial coverage (looser Quality Control). Subsequent research using Version 5 sounding and error estimates showed that tighter Quality Control performs better for data assimilation proposes, while looser Quality Control better spatial coverage) performs better for climate purposes. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 degree latitude x 0.67 degree longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates (delta)T(p) were used as the uncertainty for each measurement in the data assimilation process.

Susskind, Joel↗
NASA NTRS1976

Experiments with the fourth order GISS general circulation model

The new GISS GCM with 4th order conservative horizontal finite difference is briefly described. A simple forecast performed with real initial data indicates a significant increase in skill with respect to standard 2nd order GISS GCM. The improvement is limited by other sources of errors, especially poor initial data and vertical truncation. It is estimated that with a 4th order - 200 km grid model, errors introduced by horizontal truncation will be negligible for about a week for most synoptic scales (wavelengths larger than 1000 km).

Kalnay-Rivas, E.↗
NASA NTRS2001

AVHRR-Based Polar Pathfinder Products: Evaluation, Enhancement and Transition to MODIS

The Advanced Very High Resolution Radiometer (AVHRR)-Based Polar Pathfinder (APP) products include calibrated AVHRR channel data, surface temperatures, albedo, satellite scan and solar geometries, and cloud mask, all composited into twice-per-day images, and daily averaged fields of sea ice motion, for regions poleward of 50 latitude. Our general goals under this grant: (1) Quantify the APP accuracy and sources of error by comparing Pathfinder products with field measurements; (2) Determine the consistency of mean fields and trends in comparison with longer time series of available station data and forecast model output; (3) Investigate the consistency of the products between the different AVHRR instruments over the 1982-present period of the NOAA program; and (4) Compare and annual cycle of the APP products with MODIS to establish a baseline for extending Pathfinder-type products into the new ESE period.

Fowler, Charles↗
NASA NTRS2015

Monitoring Changes of Tropical Extreme Rainfall Events Using Differential Absorption Barometric Radar (DiBAR)

This work studies the potential of monitoring changes in tropical extreme rainfall events such as tropical storms from space using a Differential-absorption BArometric Radar (DiBAR) operating at 50-55 gigahertz O2 absorption band to remotely measure sea surface air pressure. Air pressure is among the most important variables that affect atmospheric dynamics, and currently can only be measured by limited in-situ observations over oceans. Analyses show that with the proposed radar the errors in instantaneous (averaged) pressure estimates can be as low as approximately 5 millibars (approximately 1 millibar) under all weather conditions. With these sea level pressure measurements, the forecasts, analyses and understanding of these extreme events in both short and long time scales can be improved. Severe weathers, especially hurricanes, are listed as one of core areas that need improved observations and predictions in WCRP (World Climate Research Program) and NASA Decadal Survey (DS) and have major impacts on public safety and national security through disaster mitigation. Since the development of the DiBAR concept about a decade ago, our team has made substantial progress in advancing the concept. Our feasibility assessment clearly shows the potential of sea surface barometry using existing radar technologies. We have developed a DiBAR system design, fabricated a Prototype-DiBAR (P-DiBAR) for proof-of-concept, conducted lab, ground and airborne P-DiBAR tests. The flight test results are consistent with our instrumentation goals. Observational system simulation experiments for space DiBAR performance show substantial improvements in tropical storm predictions, not only for the hurricane track and position but also for the hurricane intensity. DiBAR measurements will lead us to an unprecedented level of the prediction and knowledge on tropical extreme rainfall weather and climate conditions.

Lin, Bing↗
NASA NTRS2016

Remote Sensing Global Surface Air Pressure Using Differential Absorption BArometric Radar (DiBAR)

Tropical storms and severe weathers are listed as one of core events that need improved observations and predictions in World Meteorological Organization and NASA Decadal Survey (DS) documents and have major impacts on public safety and national security. This effort tries to observe surface air pressure, especially over open seas, from space using a Differential-absorption BArometric Radar (DiBAR) operating at the 50-55 gigahertz O2 absorption band. Air pressure is among the most important variables that affect atmospheric dynamics, and currently can only be measured by limited in-situ observations over oceans. Analyses show that with the proposed space radar the errors in instantaneous (averaged) pressure estimates can be as low as approximately 4 millibars (approximately 1 millibar under all weather conditions). With these sea level pressure measurements, the forecasts of severe weathers such as hurricanes will be significantly improved. Since the development of the DiBAR concept about a decade ago, NASA Langley DiBAR research team has made substantial progress in advancing the concept. The feasibility assessment clearly shows the potential of sea surface barometry using existing radar technologies. The team has developed a DiBAR system design, fabricated a Prototype-DiBAR (P-DiBAR) for proof-of-concept, conducted lab, ground and airborne P-DiBAR tests. The flight test results are consistent with the instrumentation goals. Observational system simulation experiments for space DiBAR performance based on the existing DiBAR technology and capability show substantial improvements in tropical storm predictions, not only for the hurricane track and position but also for the hurricane intensity. DiBAR measurements will lead us to an unprecedented level of the prediction and knowledge on global extreme weather and climate conditions.

Lin, Bing↗
NASA NTRS1975

A comparison of Skylab S-193 and aircraft views of surface roughness and a look toward SEASAT

An extensive aircraft underflight program was conducted along the Skylab groundpath for the purpose of documenting wind, wave, and atmospheric conditions affecting the amplitude of the active and passive microwave signatures. The S-193 microwave system senses a roughness parameter at the ocean surface that is proportional to the surface windspeed. The aircraft program is described, and some comparisons of satellite and aircraft results are presented. The principal result of the comparison of active radar is that direct inferences of the surface windspeed are possible, but subject to considerable scatter, and that this scatter appears to be due to interaction between long gravity and short Bragg waves and backscatter due to rain as well as errors in correcting for azimuth dependence. An unforeseen opportunity to observe a Pacific hurricane by both Skylab and NOAA aircraft has contributed to the development of a simplified wave forecasting scheme applicable to hurricanes, and more general conditions, which combines the better qualities of both spectral and height/period forecasting techniques. Horizontal polarization data obtained by the aircraft in Hurricane Ava, and in other experiments, which led to this development are presented.

Ross, D.↗
NASA NTRS1975

Atmospheric response to variations in sea-surface temperature

A two-week prediction experiment was performed with the GISS atmospheric model on a global data set beginning 20 December 1972 to test the sensitivity of the model to sea-surface temperature (SST) variations. Use of observed SST's in place of climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a marked local effect on predicted precipitation over the ocean, with enhanced convection over warm SST anomalies. However, use of observed SST's did not lead to any detectable general improvement in forecast skill. The influence of the SST anomalies on daily predicted fields of pressure and geopotential was small up to about one week compared with the growth of prediction error, and no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea-level pressure and 500 mb height predicted by the model were similarly insensitive to the SST anomalies.

Spar, J.↗