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NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS1994

Calibration of TOPEX/POSEIDON at Platform Harvest

We present estimates for the mean bias of the TOPEX/POSEIDON NASA altimeter (ALT) and the Centre National d'Etudes Spatiales altimeter (SSALT) using in-situ data gathered at Platform Harvest during the first 36 cycles of the mission. Data for 21 overflights of the ALT and six overflights of the SSALT have been analyzed. The analysis includes an independent assessment of in-situ measurements of sea level, the radial component of the orbit, wet tropospheric path delay, and ionospheric path delay. (The sign convention used is such that, to correct the geophysical data record values for sea level, add the bias algebraically. Unless otherwise stated, the uncertainty in a given parameter is depicted by +/- sigma(sub x), where sigma(sub x) is the sample standard deviation of x about the mean.) Tide gauges at Harvest provide estimates of sea level with an uncertainty of +/- 1.5 cm. The uncertainty in the radial component of the orbit is estimated to be +/- 1.3 cm. In-situ measurements of tropopsheric path delay at Harvest compare to within +/- 1.3 cm of the TOPEX/POSEIDON microwave radiometer, and in-situ measurements of the ionospheric path delay compare to within -0.4 +/- 0.7 cm of the dual-frequency ALT and 1.1 +/- 0.6 cm of Doppler orbitography and radiopositioning integrated by satellite. We obtain mean bias estimates of -14.5 +/- 2.9 cm for the ALT and +0.9 +/- 3.1 cm for the SSALT (where the uncertainties are based on the standard deviation of the estimated mean (sigma(sub bar x/y), which is derived from sample statistics and estimates for errors that cannot be observed). These results are consistent with independent estimates for the relative bias between the two altimeters. A linear regression applied to the complete set of data shows that there is a discernable secular trend in the time series for the ALT bias estimates. A preliminary analysis of data obtained through cycle 48 suggests that the apparent secular drift may be the result of a poorly sampled annual signal.

Christensen, E. J.↗

NASA NTRS2013

Direct Top-down Estimates of Biomass Burning CO Emissions Using TES and MOPITT Versus Bottom-up GFED Inventory

In this study, we utilize near-simultaneous observations from two sets of multiple satellite sensors to segregate Tropospheric Emission Spectrometer (TES) and Measurements of Pollution in the Troposphere (MOPITT) CO observations over active fire sources from those made over clear background. Hence, we obtain direct estimates of biomass burning CO emissions without invoking inverse modeling as in traditional top-down methods. We find considerable differences between Global Fire Emissions Database (GFED) versions 2.1 and 3.1 and satellite-based emission estimates in many regions. Both inventories appear to greatly underestimate South and Southeast Asia emissions, for example. On global scales, however, CO emissions in both inventories and in the MOPITT-based analysis agree reasonably well, with the largest bias (30%) found in the Northern Hemisphere spring. In the Southern Hemisphere, there is a one-month shift between the GFED and MOPITT-based fire emissions peak. Afternoon tropical fire emissions retrieved from TES are about two times higher than the morning MOPITT retrievals. This appears to be both a real difference due to the diurnal fire activity variations, and a bias due to the scarcity of TES data.

bias↗

NASA NTRS2022

Introducing and Evaluating the Climate Hazards Center IMERG with Stations (CHIMES) Timely Station-Enhanced Integrated Multisatellite Retrievals for Global Precipitation Measurement

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim ’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC’s curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month −1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month −1.

Chris C funk↗

NASA NTRS1995

Combinations of Earth Orientation Observations: SPACE94, COMB94, and POLE94

A Kalman filter has been used to combine all publicly available, independently determined measurements of the Earth's orientation taken by the modern, space-geodetic techniques of very long baseline interferometry, satellite laser ranging, lunar laser ranging, and the global positioning system. Prior to combining the data, tidal terms were removed from the UT1 measurements, outlying data points were deleted, series-specific corrections were applied for bias and rate, and the stated uncertainties of the measurements were adjusted by multiplying them by series-specific scale factors. Values for these bias- rate corrections and uncertainty scale factors were determined by an iterative, round-robin procedure wherein each data set is compared, in turn, to a combination of all other data sets. When applied to the measurements, the bias-rate corrections thus determined make the data sets agree with each other in bias and rate, and the uncertainty scale factors thus determined make the residual of each series (when differenced with a combination of all others) have a reduced chi-square of one. The corrected and adjusted series are then placed within an IERS reference frame by aligning them with the IERS Earth orientation series EOP (IERS)90C04. The result of combining these corrected, adjusted and aligned series is designated SPCE94 and spans October 6.0, 1976 to January 27.0, 1995 at daily intervals.

SPACE94 COMB94↗

NASA NTRS1985

DE 1 RIMS operational characteristics

The Retarding Ion Mass Spectrometer (RIMS) on the Dynamics Explorer 1 spacecraft observes both the thermal and superthermal (50 eV) ions of the ionosphere and inner magnetosphere. It is capable of measuring the detailed species distribution function of these ions in many cases. It was equipped with an integral electrometer to permit in-flight calibration of the detector sensitivities and variations thereof. A guide to understanding the RIMS data set is given. The reduction process from count rates to physical quantities is discussed in some detail. The procedure used to establish in-flight calibration is described, and results of a comparison with densities from plasma wave measurements are provided. Finally, a discussion is provided of various anomalies in the data set, including changes of channeltron efficiency with time, spin modulation of the axial sensor heads, apparent potential differences between the sensor heads, and failures of the radial head retarding potential sweep and of the -Z axial head aperture plane bias. Studies of the RIMS data set should be conducted only with a thorough awareness of the material presented here, or in collaboration with one of the scientists actively involved with RIMS data analysis.

Olsen, R. C.↗

NASA NTRS1997

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.

Carder, Kendall L.↗

NASA NTRS1996

Combinations of Earth Orientation Measurements: SPACE94, COMB94, and POLE94

A Kalman filter has been used to combine independent measurements of the Earth's orientation taken by the space-geodetic observing techniques of lunar laser ranging, satellite laser ranging, very long baseline interferometry, and the Global Positioning System. Prior to their combination, the data series were adjusted to have the same bias and rate, the stated uncertainties of the measurements were adjusted, and data points considered to be outliers were deleted. The resulting combination, SPACE94, consists of smoothed, interpolated polar motion and UT1-UTC values spanning October 6, 1976, to January 27, 1995, at 1-day intervals. The Kalman filter was then used to combine the space-geodetic series comprising SPACE94 with two different, independent series of Earth orientation measurements taken by the technique of optical astrometry. Prior to their combination with SPACE94, the bias, rate and annual term of the optical astrometric series were corrected, the stated uncertainties of the measurements were adjusted, and data points considered to be outliers were deleted. The adjusted optical astrometric series were then combined with SPACE94 in two steps: (1) the Bureau International de l'Heure (BIH) optical astrometric series was combined with SPACE94 to form COMB94, a combined series of smoothed, interpolated polar motion and UT1-UTC values spanning January 20, 1962, to January 27, 1995, at 5-day intervals, and (2) the International Latitude Service (ILS) optical astrometric series was combined with COMB94 to form POLE94, a combined series of smoothed, interpolated polar motion values spanning January 20, 1900, to January 21, 1995, at 30.4375-day intervals.

Gross, Richard S.↗

NASA NTRS2003

Unexplained Discontinuity in the US Radiosonde Temperature Data: Stratosphere - Part 2

In part I of this paper, the United States (US) radiosonde temperature data are shown to have significant and unexplained inhomogeneities in the mid-troposphere. This part discusses the differences between observations taken at 0 and 12 UTC especially in the stratosphere by the Vaisala RS80 radiosondes that are integrated within the National Weather Service's (NWS) Micro-ART system. The results show that there is a large maxima in the horizontal distribution of the monthly means of the 0/12 UTC differences over the central US that is absent over Canada and this maxima is as large as 5 C at 10 hPa. The vertical profiles of the root-mean-square of the monthly means are much larger in the US than those else where. The data clearly shows that the 0/12 UTC differences are largely artificial especially over the central US and originate in the post processing software at observing stations, thus confirming the findings in part I. Special flight data from the NWS's test facility at Sterling, Va. have been obtained. This data can be used to deduce the bias correction applied by Vaisala's post processing system. By analyzing the correction data, it can be shown that the inconsistencies with non-US Vaisala RS80 data as well as most of the large 0/12 UTC differences over the US can be accounted for by multiplying the reported elapsed time (i.e. time since launch) by the factor which is incorrectly applied by the post processing software. After being presented with the findings in this paper, Vaisala further isolated the source of the inconsistencies to a software coding error in the radiation bias correction scheme. The error effects only the software installed at US stations.

Redder, Christopher R.↗

NASA NTRS1995

Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data

Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.

Li, Zhanqing↗

NASA NTRSDate not supplied

Leveraging Human Performance Data to Change the Narrative that People are the Safety Problem

The study of errors and failure has a long and productive history in the behavioral sciences. By studying how systems fail, we rule out various mechanisms for how those systems might work, thereby refining our theories of how they actually work. Human performance, however, includes more than errors; human performance comprises both failures and successes. A systematic bias to collect and analyze data only on error affects the decisions we make as a community by promoting the narrative that “people are the safety problem.” This narrative manifests in both obvious and subtle ways in the design of systems intended for human use. When the only safety data that are available are about human failure, then “data-driven” designs can only consider that humans fail. Changing this narrative will depend on new data and new ways to examine data – specifically, data on the processes by which human create and contribute to safety. An alternate narrative is that people represent a primary source of safety, through their capability to anticipate, monitor for, respond to, and learn from expected and unexpected change. This presentation will describe research efforts to expand the range of safety-relevant events to include not just rare safety failures but frequent safety successes. These efforts include use of data from both operations and simulations to develop methods and metrics for learning from structured observation, self-report, and system data.

Jon Holbrook↗

NASA NTRSDate not supplied

An Investigation Into Possible Systematic Effects on Neutron Star Radius Estimates using NICER-like Synthetic Data

Neutron star cores contain the densest matter in the observable universe. The state of this matter is of interest in numerous fields, but laboratory experiments cannot explore this matter. Although the composition of the matter would be of great interest, macroscopic observables such as the neutron star mass-radius relation depend primarily on the equation of state (EOS). As a result, precise and reliable radius measurements would be valuable in constraining the EOS. However, most attempts at radius measurements are susceptible to systematic errors, meaning the inferred radius can be significantly biased even though the fit to the data appears to be statistically good. Previous studies suggested that radii inferred using X-ray data provided by NASA’s NICER mission may be more immune from such systematic errors. This is in part because, compared with previous measurements that obtained averaged spectra and fluxes, NICER observes millisecond pulsars by timing the photons so precisely, it is possible to obtain the spectrum as a function of rotational phase and see variations such as heated regions on the star rotate into and out of view hundreds of times per second. This extra information seems promising to break degeneracies and mitigate systematic errors, but a more in-depth study is necessary. We report the first steps of that study, in which we generate NICER-like synthetic data and determine the quality of fit and bias in radius obtained when we fit the data using a model different from the model used to generate the data.

Isiah Holt↗

NASA NTRS1989

Comment on 'Observations of Low-Latitude Electron Precipitation' by R. Lieu, J. Watermann, K. Wilhelm, J. J. Quenby, and W. I. Axford

Observations made by an electron spectrometer aboard Spacelab 1 and presented by Lieu et al. (1988) are examined critically. The precipitation of electrons in the energy range of 0.1-12.5 keV was measured on December 6 and 7, 1983. Data for 16 passes near 240 km altitude, between + and - 30 deg geographic latitude, outside the South Atlantic Anomaly were included. It is argued that there is no geophysical confirmation of the large electron fluxes reported by Lieu et al. In their response, Lieu et al. discuss the sampling bias in the Spacelab 1 data and the magnetic shielding deficiencies of the calibration facility below about 500 eV.

Rassoul, H. K.↗

NASA NTRS2014

Optical Efficiency and R(T,I) Measurements of ACTPol TESes Using Time Domain Multiplexing Electronics

We present new data on feedhorn-coupled transition-edge sensor devices fabricated for the second-generation receiver (ACTPol) for the Atacama cosmology telescope (ACT). First, we describe optical efficiency measurements of the latest ACTPol detector wafer, which has a 86±15% average optical efficiency. Next, we discuss measurements of the TES resistance as a function of temperature and bias current ( R(T,I) ) using the ACTPol time-domain multiplexing electronics. Qualitative agreement between R(T,I) data at low bias current and the two-fluid model prediction is shown. Using the two-fluid model and low bias current R(T,I) data, α and β at our operating bias current are calculated.

Atacama cosmology telescope (ACT)↗

NASA NTRS2019

CloudSat Bias on Falling Snow Estimates Over the Daylight Only Operational Period (2012-2019)

Falling snow is a key component for the global atmospheric, hydrological and energy cycles, and its retrieval from space-based observations represents the best current capability to evaluate it globally. The Global Precipitation Measurement (GPM) Mission Core Observatory, launched in 2014, together with its constellation sensors, can provide quasi-global precipitation estimates every 30 minutes (for level 3 products). Evaluation and validation efforts for such products are crucial, and for global evaluations, one of the most suitable instruments is the Cloud Profiling Radar (CPR) on board CloudSat, which is sensitive to light rain and falling snow. However, due to a battery anomaly in 2011, during its period of overlapping observations with GPM the CPR has operated in a Daylight Only Operations mode (DO-Op) in which it makes measurements primarily during only the daylit portion of its orbit. The goal of this work is to estimate biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat's snowfall measurements during its Full Operations (Full-Op) period from 2006 to 2010 to evaluate the impact DO-Op mode would have had during this period. Results indicate that omitting the nocturnal component of the diurnal cycle of snowfall has nonnegligible impact on snowfall amounts in some regions. The lack of nighttime data during DO-Op biases the latitudinally averaged mean snowfall rates as well as some regional values. Hemispheric differences in bias may be due to more pronounced diurnal variability in the northern hemisphere owing to more prevalent land surface versus the southern hemisphere. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

Milani, Lisa↗

NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗

NASA NTRS1990

Flight motor set 360L006 (STS-34). Volume 1: System overview

Flight motor set 360L006 was launched at approximately 11:54 a.m. Central Daylight Time (CDT) on 18 October 1989 as part of NASA space shuttle mission STS-34. As with all previous redesigned solid rocket motor launches, overall motor performance was excellent. All ballistic contract end item (CEI) specification parameters were verified with the exceptions of ignition interval and rise rates. Ignition interval and rise rates could not be verified due to the elimination of developmental flight instrumentation from fourth flight and subsequent, but the low sample rate data that were available showed nominal propulsion performance. All ballistic and mass property parameters closely matched the predicted values and were well within the required CEI specification levels that could be assessed, with the exception of the RH-motor vacuum-delivered specific impulse. It exceeds the upper-limit CEI specification due to a bias imposed on the raw data by the OPT/Taber gage measurement differences. Evaluation of the ground environment instrumentation measurements again verified thermal model analysis data and showed agreement with predicted environmental effects. No launch commit criteria thermal violations occurred. Postflight inspection again verified superior performance of the insulation, phenolics, metal parts, and seals. Postflight evaluation indicated both nozzles performed as expected during flight, although splashdown loads tore the left-hand, 45-deg actuator bracket from the nozzle. All combustion gas was contained by insulation in the field and nozzle-to-case joints. Recommendations were made concerning improved thermal modeling and measurements. The rationale for these recommendations, the disposition of all anomalies, and complete result details are contained.

Garecht, Diane M.↗

NASA NTRS2010

Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of assimilating AOD retrievals from MODIS (on both Aqua and TERRA satellites) from AERONET for validation. The impact on the GEOS-5 Aerosol Forecasting will be fully documented.

da Silva, Arlindo↗