National Environmental Satellite, Data, And Information Service Research Articles

JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP

AbstractA three‐dimensional ensemble‐variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System‐Aerosols (GEFS‐Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS‐Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S‐NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent‐linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package‐based GEFS‐Aerosols to account for aerosol emission uncertainty. One‐month retrospective and three‐month near‐real‐time experiments consistently showed improved GEFS‐Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium‐Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error‐spread consistency and further improved AOD assimilation. The valid‐time‐shifting ensemble approach in a cost‐effective manner of populating background ensembles showed positive impacts on AOD assimilation.

Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models

Soil Moisture is an important variable for hydrological, meteorological and agricultural studies and applications. The Soil Moisture Operational Products System (SMOPS) was developed by the National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data, and Information Service (NESDIS) to operationally provide an integrated satellite soil moisture data product. The Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture retrieval is an important component of the currently operational SMOPS. This study is proposed to refine the AMSR2 data product using an optimal machine learning model, and this first paper of the two-part series is to intercompare the six commonly-used machine learning models including multiple linear regression (MLR), Regression Tree (RRT), Random Forest (RFT), Gradient Boosting (GBR), Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN). Results indicate that all of the six approaches can preserve the reference data information beyond the training time period, which ensures them to predict past and future satellite retrievals without a new training procedure. Relative to other models, the XGB method is more successful to respect to the reference data Soil Moisture Active Passive (SMAP) and the in-situ observations from the U. S. Department of Agriculture Soil Climate Analysis Network (SCAN). It has a good implication on the implementation of the XGB model to refine the AMSR2 soil moisture retrievals in the second paper.

Identification of Summer Monsoon Onset over Nepal by using Satellite-Derived OLR Data

Summer monsoon onset is considered as the beginning of the rainy season in South Asia. In Nepal, summer monsoon onsets from eastern part and ~80% of the annual rainfall occurs during summer monsoon season (i.e., June to September). The monsoon onset is one of the crucial elements for the socio-economic development as the majority of the population depends primarily on rain fed agriculture systems for their livelihood. For identification of the monsoon onset date over eastern Nepal, daily mean satellite-derived Outgoing Long-wave Radiation (OLR) data (10 × 10 spatial resolution) obtained from National Environmental Satellite Data and Information Service (NESDIS), NOAA is used. Long term OLR data from 1979 to 2019 for March to June is analyzed to study monsoon progression towards eastern Nepal. The rainfall amount obtained from rain gauge stations is compared with the OLR data, which depicts a higher correlation in the southern part of Eastern and Central Nepal than in the northern regions. A convective episode signifies the drop in OLR by about 45 Wm-2 from 10 May to 10 June. Further, the relation between the date of characteristic fall of OLR below 250Wm-2 and monsoon onset date announced by MFD (Meteorological Forecasting Division) for the study period is evaluated to predict new onset dates over the region. The predicted onset date is found as 13th of June on average and its correlation with the MFD onset date is 0.6 (p<0.01). The study highlights the potential application of the satellite-derived OLR based method for the early prediction of onset of summer monsoon over eastern Nepal.

Motor-Tanker Coimbra - The Million Gallon Gamble

ABSTRACT In January of 1942, the M/T COIMBRA, a British flag vessel, carrying 64,800 barrels of lubricating oil and fuel oil was torpedoed by the German submarine U-123. It sank 26 NM south of Shinnecock Inlet, NY. The vessel was classified by the National Oceanic and Atmospheric Administration (NOAA) as a Remediation of Underwater Legacy Environmental Threats (RULET) vessel and was ranked the second highest pollution threat in the First Coast Guard District area of responsibility and the highest for U. S. Coast Guard Sector Long Island Sound. In 2015, Sector Long Island Sound started receiving pollution reports from the National Environmental Satellite Data and Information Service (NESDIS) via NOAA of anomalies in the vicinity of the wreck location of M/T COIMBRA. The anomalies were later confirmed by U.S. Coast Guard overflights and surface sheen sampling as oil. The Federal On-Scene Coordinator (FOSC) determined that M/T COIMBRA posed a substantial threat to the environment and opened the Oil Spill Liability Trust Fund to complete an assessment and execute an oil removal operation from the vessel that was deteriorating on the bottom of the Atlantic Ocean for 77 years. The vast number of unknowns, government regulations, technological challenges, varying weather patterns, diverse marine fauna, and lack of historical records about non-U.S. flagged vessels complicated the response to this pollution threat. Establishing a diverse team of professionals comprised of federal, academic, and state specialists, a detailed statement of work was created, and a contractor was chosen to complete objectives established by the FOSC. As a result of the operation, the U.S. Coast Guard determined the following: - Types and quantity of oil onboard the subject vessel- Best practices for the oil removal from an historic wreck- The impacts of salt water on the riveted hull structures- Challenges of the oil removal from various historical ship construction types A total of 81 out of 87 wrecks on the RULET database remain unassessed and unexplored in the United States alone. RULET wrecks and other historical polluting wrecks around the world may present a significant threat to the environment. The COIMBRA project answers some of the questions that will make future cases of these polluting historical wrecks more cost effective and less time consuming. In the best-case scenario this project will serve as an example for establishment of the framework for future projects of this nature.

Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations

Evapotranspiration (ET) is a major component of the global and regional water cycle. An operational Geostationary Operational Environmental Satellite (GOES) ET and Drought (GET-D) product system has been developed by the National Environmental Satellite, Data and Information Service (NESDIS) in the National Oceanic and Atmospheric Administration (NOAA) for numerical weather prediction model validation, data assimilation, and drought monitoring. GET-D system was generating ET and Evaporative Stress Index (ESI) maps at 8 km spatial resolution using thermal observations of the Imagers on GOES-13 and GOES-15 before the primary operational GOES satellites transitioned to GOES-16 and GOES-17 with the Advanced Baseline Imagers (ABI). In this study, the GET-D product system is upgraded to ingest the thermal observations of ABI with the best spatial resolution of 2 km. The core of the GET-D system is the Atmosphere-Land Exchange Inversion (ALEXI) model, which exploits the mid-morning rise in the land surface temperature to deduce the land surface fluxes including ET. Satellite-based land surface temperature and solar insolation retrievals from ABI and meteorological forcing from NOAA NCEP Climate Forecast System (CFS) are the major inputs to the GET-D system. Ancillary data required in GET-D include land cover map, leaf area index, albedo and cloud mask. This paper presents preliminary results of ET from the upgraded GET-D system after a brief introduction of the ALEXI model and the architecture of GET-D system. Comparisons with in situ ET measurements showed that the accuracy of the GOES-16 ABI based ET is similar to the results from the legacy GET-D ET based on GOES-13/15 Imager data. The agreement with the in situ measurements is satisfactory with a correlation of 0.914 averaged from three Mead sites. Further evaluation of the ABI-based ET product, upgrade efforts of the GET-D system for ESI products, and conclusions for the ABI-based GET-D products are discussed.

Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events

Abstract This study examines characteristics of lightning in snowfall events (i.e., thundersnow, TSSN) from the perspective of the Geostationary Lightning Mapper (GLM) and the National Environmental Satellite Data and Information Service (NESDIS) merged Snowfall Rate (mSFR) product. A thundersnow detection algorithm (TDA) was derived from the GLM and mSFR that resulted in a probability of detection (POD) of 66.7% when compared to the aviation routine weather report (METAR) reports of TSSN. However, using the TDA an additional 2175 lightning flashes within detected snowfall were identified that were not observed by the METAR reports, indicating that TSSN has been under reported in previous literature. TSSN flashes observed by GLM have mean flash areas, durations, and total optical energy outputs of 754 km2, 402 ms, and 1342 fJ, which are between the 50th and 99th percentile values for all flashes within the GLM field of view. A comparison with data from the National Lightning Detection Network (NLDN) indicated that the NLDN had at least one cloud or ground flash detection in 1709 of the 2214 flashes observed by GLM in snowfall. An average of 5.85 NLDN flashes was assigned to a single GLM flash when the NLDN flash data were constrained by the GLM flash duration and spatial footprint. Statistically significant (p < 0.01) differences in flash area and flash energy were found between flashes that were observed by the NLDN and those that were not. Additionally, when GLM was combined with the NLDN, at least 11.1% of flashes involved a tall human-made object like an antenna or wind turbine.

Investigation of high ozone events due to wildfire smoke in an urban area

Using data from the St. Luke's site in Meridian, ID (near Boise) during 2006–2017 and a 2017 summer intensive campaign, we investigate enhancements in ozone (O3) during wildfire events in an urban area. We calculate a wildfire criterion based on the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product and historically averaged PM2.5 data to determine when wildfire emissions are influencing the area (smoke vs. non-smoke events). We also use a Generalized Additive Model (GAM) to investigate anomalous sources of O3, such as wildfires, in this urban area. During the summer 2017 intensive campaign, we find that peroxyacetyl nitrate (PAN), reactive nitrogen (NOy), and maximum daily 8 h average (MDA8) O3 show significant enhancements during smoke events compared with non-smoke periods (65%, 42%, and 32%, respectively). We calculate the 95% confidence interval of ΔPM2.5/ΔCO, ΔNOy/ΔCO, ΔPAN/ΔNOy, and ΔPAN/ΔCO enhancement ratios (ERs) to be 0.129–0.144 μg/m3/ppbv, 0.018–0.022 ppbv/ppbv, 0.152–0.192 ppbv/ppbv, and 3.04–3.76 ppbv/ppmv, respectively, for wildfire smoke influenced events. We also observe an enhancement in O3 production up to PM2.5 concentrations of 60–70 μg/m3 in smoke, after which we see a reduction in average MDA8 O3 mixing ratios. We use the four highest O3 events during summer 2017 as case studies to examine the highly variable conditions due to the influence of wildfire smoke in an urban area. In two cases, we investigate smoke days that show significant O3 enhancement and moderate PM2.5 concentrations. These cases suggest that ERs, such as ΔPM2.5/ΔCO and ΔNOy/ΔCO, are less useful in determining the influence of wildfire smoke in an urban area on moderate smoke days. Another case shows reduced O3 production during a very high, 3-day smoke event (PM2.5 > 70 μg/m3). After this high smoke period, a 20 ppbv enhancement in MDA8 O3 is observed in moderate smoke. These results indicate that wildfire-influenced O3 enhancements are highly variable in urban areas but generally increase up to around 60 μg/m3 of PM2.5, after which they decrease at very high smoke concentrations. This study also suggests that multiple tracer measurements are needed to fully characterize wildfire plumes in urban areas.

Connecting smoke plumes to sources using Hazard Mapping System (HMS) smoke and fire location data over North America

Abstract. Fires represent an air quality challenge because they are large, dynamic and transient sources of particulate matter and ozone precursors. Transported smoke can deteriorate air quality over large regions. Fire severity and frequency are likely to increase in the future, exacerbating an existing problem. Using the National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke data for North America for the period 2007 to 2014, we examine a subset of fires that are confirmed to have produced sufficient smoke to warrant the initiation of a U.S. National Weather Service smoke forecast. We find that gridded HMS-analyzed fires are well correlated (r= 0.84) with emissions from the Global Fire Emissions Inventory Database 4s (GFED4s). We define a new metric, smoke hours, by linking observed smoke plumes to active fires using ensembles of forward trajectories. This work shows that the Southwest, Northwest, and Northwest Territories initiate the most air quality forecasts and produce more smoke than any other North American region by measure of the number of HYSPLIT points analyzed, the duration of those HYSPLIT points, and the total number of smoke hours produced. The average number of days with smoke plumes overhead is largest over the north-central United States. Only Alaska, the Northwest, the Southwest, and Southeast United States regions produce the majority of smoke plumes observed over their own borders. This work moves a new dataset from a daily operational setting to a research context, and it demonstrates how changes to the frequency or intensity of fires in the western United States could impact other regions.

Remotely-Sensed Oil and Shoreline Interaction Modeling

ABSTRACT No. 2017-233 The broad adoption of remotely sensed data and derivative products from satellite and aerial platforms available to describe the distribution of spilled oil on the water surface was an important factor during Deepwater Horizon (DWH) oil spill both for tactical response and damage assessment. The availability and utility of these data in describing on-water oil distribution provide strong temptation to make estimates about on-shoreline oil distribution. The mechanisms by which floating oil interact with the shoreline, however, are extremely complex, heterogeneous at fine spatial scales, and generally not well described or quantified beyond broad conceptual or spill-specific empirical models. In short, oil on water does not necessarily lead to oil on adjacent shorelines. We combine data derived from NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity describing the remotely-sensed, daily composite anomaly polygons representing oil on water over multiple months with ground observations made in the field, collocated in time and space extracted from a newly compiled database of ground survey data (SCAT, NRDA and others) from the northwestern Gulf of Mexico. Because this new compiled dataset is very large (100,000s of observations) and spans a wide range of habitats, geography, and time, it is particularly suitable for inference and predictive modeling. We use these combined datasets to make inference about the relative influence on shoreline oiling probability and loading of distance from on-water oil observation via multiple distance metrics, shoreline morphology, water levels and ranges, wind direction and speed, wave energy, shoreline aspect and geometry. We also construct predictive models using machine-learning modeling methods to make predictions about shoreline oiling probability given observed distributions of on-water oil. The importance of this work is three part: firstly, the relationships between these parameters can assist hind-cast modeling of shoreline oiling probability for the Deepwater Horizon oil spill. Secondly, these data and models can permit similar modeling for future spills. Lastly, we propose that this dataset serve as a nucleus that can be expanded using data from subsequent or future spills to allow iteratively improvements in shoreline oil probability modeling using remotely sensed data, as well as an improved understanding of oil-shoreline interactions more generally.

A New High-Resolution Sea Surface Temperature Blended Analysis

AbstractThe National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ∼0.02 K, with a robust standard deviation of ∼0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of microwave SST products from low-Earth-orbiting platforms [e.g., Global Change Observation Mission for Water-1 (GCOM-W1)] in order to improve the resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper-ocean heating.

Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI

AbstractAs the first of the next-generation geostationary meteorological satellites, Himawari-8 was successfully launched in October 2014 by the Japan Meteorological Agency (JMA) and placed over the western Pacific Ocean domain at 140.7°E. It carries the Advanced Himawari Imager (AHI), which provides full-disk images of Earth at 16 bands in the visible and infrared domains every 10 min. Efforts are currently ongoing at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) to assimilate Himawari-8 AHI radiance measurements into the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation analysis system (GSI). All software development within the GSI to allow for assimilation of Himawari-8 AHI radiance has been completed.This study reports on the assessment of AHI preassimilation data quality by comparing observed clear-sky ocean-only radiances to those simulated using collocated ECMWF analysis, as well as describing procedures implemented for quality control. The impact of the AHI data assimilation on the resulting analyses and forecasts is then assessed using the NCEP Global Forecast System (GFS). A preliminary assessment of the assimilation of AHI data from infrared water vapor channels and atmospheric motion vectors (AMVs) on top of the current global observing system shows neutral to marginal positive impact on analysis and forecast skill relative to an assimilation without AHI data. The main positive impact occurs for short- to medium-range forecasts of global upper-tropospheric water vapor. The results demonstrate the feasibility of direct assimilation of AHI radiances and highlight how humidity information can be extracted within the assimilation system.

Progress and Developments of Downburst Prediction Applications of GOES

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has developed and evaluated a suite of products that assess convective storm–generated downburst potential derived from Geostationary Operational Environmental Satellite-13–15 (GOES-13–15). The existing suite of downburst prediction algorithms employs the GOES sounder to calculate risk based on conceptual models of favorable environmental thermodynamic profiles for downburst occurrence. A diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI), is designed to identify attributes of a favorable downburst environment: 1) the presence of large CAPE and 2) the presence of a surface-based or elevated mixed layer with a large temperature lapse rate. This paper provides an updated assessment of the MWPI algorithm, presents case studies demonstrating effective operational use of the MWPI product, and presents validation results for the Great Plains and mid-Atlantic coastal region of the United States. MWPI data were collected for downburst events that occurred during the convective seasons of 2007–13 and were validated against surface observations of convective wind gusts as recorded by wind sensors in high quality mesonetworks over the southern Great Plains and the Chesapeake Bay region. Favorable validation results include a correlation greater than 0.6 and low mean error [<0.1 knot (kt; where 1 kt = 0.51 m s−1)] between MWPI values and measured confirmed downburst wind speeds over contrasting climate regions of the continental United States. Case studies over the mid-Atlantic region and northern Florida highlight the adaptability of the MWPI algorithm to severe convective storm forecasting and warning operations.

An integrated method to improve the GOES Imager visible radiometric calibration accuracy

A variety of vicarious calibration methods over different reference targets have been studied at National Oceanic and Atmospheric Admission (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS) to provide sensor degradation information for the GOES Imager visible channels which do not have any on-board calibration devices. To meet the increasing demand for more accurate satellite measurements, an integrated method is developed mainly to improve the relative calibration accuracy by combining the results from different vicarious methods. In this study, three commonly used vicarious calibration methods, the desert, Deep Convective Cloud (DCC) and ray-matching methods, are applied to the GOES-12 Imager visible data to describe the integrated method. The integrated method first combines the normalized observations of each individual method by anchoring the results on the first day of satellite operation, and then removes the deviated combined observations using a recursive filtering method. This integration is based on the fact that the GOES sensor trending functions from different methods are very similar within 1% difference over the 7-year study period. The trending uncertainty of the integrated method is less than that of each individual one. The absolute calibration of the integrated method can be achieved by generating the calibration coefficients using the reflectance of reference targets which are well characterized with the Aqua MODIS Collection 6 (C6) data. It is found that there is less than 1% difference between the calibration coefficients derived with the DCC and desert reference targets. It is expected that this integrated method will be a useful tool to validate the GOES-R ABI on-board radiometric calibration accuracy for the solar reflective channels.

National Environmental Satellite, Data, and Information Service: Providing Global Observations to Understand Earth Science Systems

AbstractThe National Oceanic and Atmospheric Administration's (NOAA) mission is to monitor, assess, and predict an ever-changing environment that extends from the bottom of the ocean to the surface of the sun. The National Environmental Satellite, Data, and Information Service (NESDIS) operates a fleet of environmental satellites that provide observations and measurements critical for this assessment of the Earth-sun system, including data for real-time monitoring of severe weather events and for developing numerical prediction models and forecasts. NESDIS also archives the data collected so users can access historical satellite data on atmosphere, land, and oceans dating back to the 1970s and in situ data going back centuries in order to improve our understanding of Earth's climate system. Because the World Ocean is one of the most important drivers of weather and climate on the planet, NOAA satellites provide global data for operational products that affect Earth's ocean ecosystems, including coral bleaching alert areas, detecting harmful algal blooms, and monitoring sea ice. With its high-volume data centers, NESDIS provides access to the world's most comprehensive sources of marine environmental data and information. This paper provides a brief history of NOAA satellites and an overview of the satellites currently in operation. Then, it focuses on satellite data and products that come together to monitor Earth's oceans and provide support for tropical storm monitoring, El Niño Southern Oscillation research, and ocean ecosystem monitoring. The paper concludes with a look toward NOAA's next generation of satellites that will be launching in the coming years and their effect on ocean monitoring.

Tactical Predictions of Shoreline Oiling Probability via Machine Learning Models and Satellite-Derived Surface Oil Analysis Products

ABSTRACT For significant oil spills in remote areas with complex shoreline geometry, apportioning Shoreline Cleanup Assessment Technique (SCAT) survey effort is a complicated and difficult task. Aerial surveys are often used to select shoreline areas for ground survey after an initial prioritization based upon anecdotal reports or trajectory models, but aerial observers may have difficulty locating cryptic surface shoreline oiling in vegetated or other complex environments. In dynamic beach environments, stranded shoreline oiling may be rapidly buried, making aerial observation difficult. A machine learning-based model is presented for estimating shoreline oiling probabilities via satellite-derived surface oil analysis products, wind summary data, and shoreline habitat type and geometry data. These inputs are increasingly available at spatial and temporal scales sufficient for tactical use, enabling model predictions to be generated within hours after satellite remote sensing products are available. The model was constructed using SCAT data from the Deepwater Horizon oil spill, satellite-derived surface oil analysis products generated during the spill by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity, and available shoreline geometry, character, and other preexisting data. The model involves the generation of set of spatial indices of relative over-water proximity of surface oil slicks based upon the satellite-derived analysis products. The model then uses boosted regression trees (BRT), a flexible and relatively recently developed modeling methodology, to generate calibrated estimates of probability of subsequent shoreline oiling based upon these indices, wind climatological data over the time period of interest, and other shoreline data. The model can be implemented via data preparation in any Geographic Information System (GIS) software coupled with the open-source statistical computing language, R. The model is entirely probabilistic and makes no attempt to reproduce the physics of oil moving through the environment, as do trajectory models. It is best used in concert with such models to make estimates at different spatial scales, or when time and data requirements make implementation of fine-scale trajectory modeling impractical for tactical use. The details of model development implementation and assessments of model performance and limitations are presented.

Adaptive Reduction of Striping for Improved Sea Surface Temperature Imagery from Suomi National Polar-Orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS)

Abstract The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)’s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%.

Accuracy Assessment of Sea Surface Temperature from NOAA/AVHRR Data in the Seas around Korea and Error Characteristics

Sea Surface Temperatures (SSTs) using the equations of NOAA (National Oceanic and Atmospheric Administration) / NESDIS (National Environmental Satellite, Data, and Information Service) were validated over the seas around Korea with satellite-tracked drifter data. A total 1,070 of matchups between satellite data and drifter data were acquired for the period of 2009. The mean rms errors of Multi- Channel SSTs (MCSSTs) and Non-Linear SSTs (NLSSTs) were evaluated to, in most of the cases, less than <TEX>$1^{\circ}C$</TEX>. However, the errors revealed dependencies on atmospheric and oceanic conditions. For the most part, SSTs were underestimated in winter and spring, whereas overestimated in summer. In addition to the seasonal characteristics, the errors also presented the effect of atmospheric moist that satellite SSTs were estimated considerably low (<TEX>$-1.8^{\circ}C$</TEX>) under extremely dry condition (<TEX>$T_{11{\mu}m}-T_{12{\mu}m}$</TEX> < <TEX>$0.3^{\circ}C$</TEX>), whereas the tendency was reversed under moist condition. Wind forcings induced that SSTs tended to be higher for daytime data than in-situ measurements but lower for nighttime data, particularly in the range of low wind speeds. These characteristics imply that the validation of satellite SSTs should be continuously conducted for diverse regional applications.

Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System

Abstract A two-season Observing System Experiment (OSE) was used to quantify the impacts of assimilating the Advanced Scatterometer (ASCAT) surface winds product distributed by the European Organization for the Exploitation of Meteorological Satellites (EUMESAT) and the National Environmental Satellite, Data, and Information Service (NESDIS). The ASCAT wind retrievals were provided by the Royal Netherlands Meteorological Office (KNMI) and the 50-km resolution ASCAT products were assimilated. The impact of assimilating the ASCAT surface wind product in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was assessed by comparing the forecast results through 168 h for the months of August 2008 and January 2009. The NCEP GDAS/GFS was used, at a resolution of T382–64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the ASCAT surface winds. Quality control procedures required to assimilate the ASCAT surface winds are discussed. Anomaly correlations (ACs) of geopotential height forecasts as well as geographic distribution of AC of geopotential height forecasts at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of forecast impact (FI) on the wind and temperature fields near the surface is also presented. The results of this study show that assimilation of the surface wind retrievals from the ASCAT sensor improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The ASCAT experiment AC scores show modest forecast improvements from days 4 through 7.

An Automated, Objective, Multiple-Satellite-Platform Tropical Cyclone Surface Wind Analysis

AbstractA method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H*Wind aircraft reconnaissance–based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h—without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within.

Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS)

Abstract Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) is a Web-based tool to monitor “model minus observation” (M − O) biases in clear-sky brightness temperatures (BTs) and sea surface temperatures (SSTs) produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). Currently, MICROS monitors M − O biases in three Advanced Very High Resolution Radiometer (AVHRR) bands centered at 3.7, 11, and 12 μm for five satellites, NOAA-16, -17, -18, -19 and Meteorological Operational (MetOp)-A. The fast Community Radiative Transfer Model (CRTM) is employed to simulate clear-sky BTs, using Reynolds SST and National Centers for Environmental Prediction Global Forecast System profiles as input. Simulated BTs are used in ACSPO for improving cloud screening, physical SST inversions, and monitoring and validating satellite BTs. The key MICROS objectives are to fully understand and reconcile CRTM and AVHRR BTs, and to minimize cross-platform biases through improvements to ACSPO algorithms, CRTM and its inputs, satellite radiances, and skin-bulk and diurnal SST modeling. Initially, MICROS was intended for internal use within the National Environmental Satellite, Data, and Information Service (NESDIS) SST team for testing and improving ACSPO products. However, it has quickly outgrown this initial objective and is now used by several research and applications groups. In particular, inclusion of double differences in MICROS has contributed to sensor-to-sensor monitoring within the Global Space-Based Intercalibration System, which is customarily performed using the well-established simultaneous nadir overpass technique. Also, CRTM scientists have made a number of critical improvements to CRTM using MICROS results. They now routinely use MICROS to continuously monitor M − O biases and validate and improve CRTM performance. MICROS is also instrumental in evaluating the accuracy of the first-guess SST and upper-air fields used as input to CRTM. This paper gives examples of these applications and discusses ongoing work and future plans.