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Cooperative Institute for Climate and Satellites MarylandCICS-MD

Director: Phil ArkinAssociate Director: E. Hugo BerberyCICS-MD SponsorsSTAR/NESDIS*CPCNCDCNODCARL*Center for Satellite Applications and Research (STAR) is the science arm of the National Environmental Satellite, Data and Information Service (NESDIS)CICS-MD Work Force40+ scientists on campus+ about 10 more hires in the next 6 months+ off-campus scientists

1Cooperative Institute for Climate and SatellitesCICS VISION

CICS performs collaborative research aimed at enhancing NOAA's ability to use satellite observations and Earth System models to advance the national climate mission, including monitoring, understanding, predicting and communicating information on climate variability and change.21. Develop innovative applications of satellite observations and advance transfer of such applications to enhance NOAA operational activities;

2. Investigate satellite observations and design information products and applications to detect, monitor and understand the impact of climate variability and change on coastal and oceanic ecosystems;

3. Identify and satisfy the satellite climate needs of users of NOAA climate information products, including atmospheric and oceanic reanalysis efforts;CICS MISSION CICS conducts research, education and outreach programs in collaboration with NOAA to:34. Improve climate forecasts on scales from regional to global through the use of satellite derived information products, particularly through participation in the NOAA/NWS/NCEP Climate Test Bed;

5. Develop and advance regional ecosystem models, particularly aimed at the Mid-Atlantic region, to predict the impact of climate variability and change on such ecosystems; and

6. Establish and deliver effective and innovative strategies for articulating, communicating and evaluating research results and reliable climate change information to targeted public audiences.CICS MISSION (cont.)4Theme 1: Climate and Satellite Research and Applications.- Development of new observing systems, or new climate observables from current systems. Theme 2: Climate and Satellite Observations and Monitoring. - Development and improvement of climate observables from current systems. - Development of all continental and global fields of climate parameters that can be used for climate analysis and climate model initialization.Theme 3: Climate Research and Modeling.- Bring together climate observables, modeling and validation in a comprehensive integrated whole. - Bring together observational products with model development efforts to enable research into the improvement of forecasts of climate system variability on space scales ranging from regional to global, and time scales from a week or two to centuries.Themes5Theme 1: Climate and Satellite Research and Applications

Development of new observing systems, or new climate observables from current systems. 6Objectives

Develop Fundamental Climate Data Record for Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) window channels.

Develop AMSU-A FCDRs for window channels

Assess the current state of land surface emissivity models/retrievalInter compare models to study their differences and similaritiesCharacterizing scan bias in AMSU-B/MHSChabitha Devaraj, Huan Meng, Ralph FerraroDevelopment of AMSU-A Fundamental CDRsWenze Yang, Huan Meng, Ralph FerraroAn Evaluation of Microwave Land Surface Emissivities for use in Precipitation AlgorithmsCecilia Hernandez, Ralph FerraroA Climate Data Record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. (Wikipedia, of course)

7Geolocation and Navigation Correction for Microwave Satellite DataIsaac MoradiGoalsInvestigate and correct different sources of the geolocation and navigation errors, includingsatellite attitudesensor mounting timing geolocation algorithm Some findingsNOAA-15 AMSU-A 2 is mounted about one degree negative along trackNOAA-17 level-1b data are not corrected for timing error at allMHS step angle was wrong in the first year of operation (1.1 instead of 10/9)

Geolocation error in NOAA level-1b data which is visible along the coastlines. Left: before correction. Right: after correctionThe difference between data from ascending and descending orbits is used to characterize the geolocation errors. Left: a difference map before correction, Right: after correction8

A Fundamental Climate Data Record for the AVHRRJonathan MittazObjectiveTo recalibrate the historic AVHRR Level1B IR radiances to be as accurate and stable as possibleResultsHistoric AVHRRs show strong scene and instrument temperature dependent biasesNew calibration removes both types of bias and indicate that the AVHRR can be accurate to ~ 0.05K and stable to < 0.1K/decade.Has significant impact on Sea Surface Temperature retrievals

Top LH panel shows errors caused by operational calibration. Top RH panel shows improvement with new calibration (MetOp-A).Left panel shows errors in SST retrievals due to operational calibration for NOAA-16 overlaid with the bias that can be removed by new calibration (black line).

9TRMM TMI V7l Over Land Rainfall Algorithm&Developing Winter Precipitation Algorithm over Land from Satellite Microwave and Field Campaign ObservationsNai-Yu Wang, Kaushik Gopalan, Ralph FerraroObjectivesThe goal is to develop a snowfall algorithm using high frequency microwave radiometer observations in conjunction with hydrometeor profiles from radar, aircraft and ground measurements.

ObjectivesRecent improvements were made to the TRMM Microwave Imager (TMI) Version 7 land rainfall algorithm global based on empirical analysis of a mutiyear global set of collocations between TMI and the precipitation radar measurements. 10

Improving Geosynchronous Satellite Rainfall Estimates Using Lightning InformationWeixin Xu, Robert Adler, and Nai-Yu WangObjectivesNOAAs GOES-R satellite (to be launched in 2016) will have first ever Geosynchronous Lightning Mapper (GLM). This study is using TRMM data to develop the basis and eventually the algorithms to combine GOES-R Infrared (IR), GLM data and microwave calibrator to provide an improved geosynchronous rainfall product for use in identifying heavy rain events and improve flood warnings. ResultsLightning occurrence has been shown to identify convective cores, especially stronger cores (higher reflectivities as indicated by TRMM radar) and area of lightning flashes is strongly related to convective rain area (TRMM radar echoes > 45 dBz). These relations will be used in development of GOES-R rain algorithm.Red: stormswith lightning

Blue: storms without lightning

11Validation of NESDIS-STAR Precipitation ProductsJohn JanowiakObjectivesProvide STAR scientists with information to improve their productsResultsBest performers are algorithms that use multiple satellites & sensorsSatellite algos. perform better than model fore-casts during warm season

12Participation in the CHUVA Field CampaignRachel Albrecht and Scott RudloskyObjectives:Investigate relationships between lightning, polarimetric radar, and satellite (GOES-R proxy) informationDeploy a Lightning Mapping Array (LMA) in Sao Paulo, BrazilCompare LMA information with a wide variety of Lightning Detection Systems

Results:LMA deployed during October 2011CHUVA IOP 1 November 2011 through 1 March 2012http://branch.nsstc.nasa.gov/splma/http://chuvaproject.cptec.inpe.br/portal/en/

LMA Sensor LocationsLMA SensorLMA Computer13Development and Validation of Satellite Active Fire Detection and Characterization AlgorithmsDr. Wilfrid Schroeder, CICS/UMDObjectives Develop, validate, and refine satellite active fire detection and characterization products Explore multi-resolution data sets (ground, airborne, and spaceborne) to map biomass burning across different spatial and temporal scalesResults Comprehensive validation and refinement of MODIS active fire product Development of new active fire products (e.g., Landsat) Development of stable long-term continental/global fire data records (e.g., GOES)

Coincident 1km Terra/MODIS and 30m ASTER Fires

High temporal and spatial resolution airborne fire science dataGlobal validation of Terra/MODIS fire product14Improving GOES-R Cloud and Precipitation Products Associated with Deep Convective Systems by using NEXRAD Radar Network over the Continental U.SZhanqing Li (UMD) & Xiquan Dong (UND)Objectives Evaluate current GOES-retrieved precipitation (SCaMPR) Develop & evaluate satellite-based cloud classification & precpitationResults New algorithm developed for estimating precipitation from warm clouds; NEXRAD demonstrates great potential for differenating precipitating and non precipitating from deep cloud anvil.

CPR rainrateMODIS COTAMSR-E rainrateWe (Chen et al. 2011, GRL) demonstrated the potential of using MODIS-derived cloud microphysics information to retrieve rainrate for warm precipitation, which are almost all missed by microwave-based AMSR-E rainfall algorithm.15Theme 2: Climate and Satellite Observations and Monitoring

Development and improvement of climate observables from current systems.

Development of all continental and global fields of climate parameters that can be used for climate analysis and climate model initialization.16Monitoring Phytoplankton Variability From SpaceChristopher BrownResultsDocumenting: Global phenology Seasonality Distribution of blooms of the coccolithophore Emiliania huxleyi

Average date of phytoplankton bloom onset for the period 1998 2009 as derived from SeaWiFS data.ObjectivesMonitor & document variability in marine algae and their productivity: Distribution Seasonality Phenology (= timing)

17Statistical Reconstruction and Analysis of Ocean Chlorophyll ConcentrationsStephanie Schollaert UzObjectivesReconstruct ocean color chlorophyll concentrations back 50 years to test the hypothesis that climate-scale oscillations are reflected in spatial patterns of phytoplankton blooms.Results SeaWiFS chlorophyll are gap-filled using MODIS Aqua to give 13 continuous years. Canonical Correlation Analysis with physical proxy data (e.g. sea-surface temperatures) is used to reconstruct chlorophyll.

Figure: Combined chlorophyll (SeaWiFS and MODIS Aqua) anomalies used to train the canonical correlation analysis algorithm. The first 4 modes capture 44% of the variability over 52 seasons (9/1997-8/2010)18The Performance of Hydrological Monthly Products using SSM/I SSMI/S sensors

Daniel Vila, Cecilia Hernandez, Ralph Ferraro, Hilawe SemunegusObjectives Improve the quality control (QC) of historical antenna temperature of SSM/I sensor Develop an improved strategy to extend the SSM/I time series into the SSMI/S eraResults The mean bias between the original and the reprocessed dataset is around 3 mm mon-1 for precipitation. That amount could be significant in long trend analysis Histogram matching technique appears as a suitable approach to extend the current dataset into SSMI/S era

Rainfall annual running mean (in mm mon-1) for the period 1992-2007 over land for three different estimates: SSMI current (reprocessed and QC checked database), GPCC and the original database.

Global Rainfall Distribution for October 2010 using SSMI/S F17

19GOALS: Derive pan-Arctic snow depth on sea ice using new, Uni. Kansas airborne snow radar systemValidate airborne data using in situ measurementsRESULTS: NASA Operation IceBridge (OIB) snow depth measurements were validated using coincident in situ data on fast ice at the Danish GreenArc ice camp in 2009 Agreement between in situ and airborne snow depths over level sea ice to better than 3 cm IceBridge Arctic 2009 data used to produce trans-Arctic snow depth (Fig. 1) Snow depth on multiyear ice is consistent with historical climatological values Snow depth on first year ice is ~50% of the climatological valueFarrell, S. L., N. Kurtz, et al. (2011), A First Assessment of IceBridge Snow and Ice Thickness Data over Arctic Sea Ice, IEEE Transactions on Geoscience & Remote Sensing, 50 (6), doi:10.1109/TGRS.2011.2170843, in press.

Kurtz, N. T. and S. L. Farrell (2011), Large-scale surveys of snow depth on Arctic sea ice from Operation IceBridge, Geophys. Res. Lett., 38, L20505, doi:10.1029/2011GL049216. http://www.agu.org/pubs/crossref/2011/2011GL049216.shtml

GreenlandArctic OceanFig. 1 IceBridge Snow Depth along 2009 aircraft flight-lines, overlaid on snow depth climatology from Warren et al., 1999

Snow Depth on Arctic Sea Ice

Sinead L. Farrell 1, 2 and Nathan T. Kurtz 1, 31 CICS/ESSIC/UMD 2 NASA GSFC 3GESTAR/Morgan State Uni.

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Important component of energy budgetMethodology: Artificial Neural Networks Trained with simulations from Rapid Radiative Transfer Model (RRTM) and clouds from CloudSat and Calipso

Implemented with: Moderate Resolution Imaging Spectroradiometer (MODIS) data, ISCCP DX and inputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA- Interim model.

Evaluated against: WCRP BSRN observations PIRATA, TOA and buoys of opportunity

Work in support of MEaSURES Activity(with Grad student E. Nussbaumer)

Longwave Radiative FluxesR. T. Pinker22

23Dr. Cezar Kongolis ResearchDevelopment of a 4-km snow depth product for the Northern HemisphereOptimal analysis approach of blending satellite and in-situ snow depth data within the Interactive Multi-Sensor Snow and Ice (IMS) Mapping product- Principal Investigator; NOAA/PSDI funding

Development of a two-source snow-vegetation energy balance modelUse of diagostic surface radiative temperature (retrieved from remote sensing) for the instantaneous estimation of surface fluxes and melt rates- Principal Investigator; USDA funding

Evaluation and improvement of NOAAs AMSU snowfall rate and detection algorithmsImprovement of the performances of the snowfall detection and rate algorithms based on AMSU data - Principal Investigator; NOAA/NESDIS/STAR funding

Development and evaluation of the GOES-R snow depth productBased on the snow-fraction snow depth relationship over sparse vegetated land areas for the future GOES-R sensor Co-Investigator; NOAA GOES-R funding

24Theme 3: Climate Research and Modeling

Bring together climate observables, modeling and validation in a comprehensive integrated whole.

Bring together observational products with model development efforts to enable research into the improvement of forecasts of climate system variability on space scales ranging from regional to global, and time scales from a week or two to centuries.25Climate AnalysesThomas M SmithObjectives

1) Improve satellite analyses

2) Use satellite analyses to help analyze historical data

3) Improved NOAA-University interactionsResultsDevelopment of historical ocean-area precipitation analysisDevelopment and improvement of other historical analysesProviding guidance to students and young researchers

First joint empirical orthogonal function of historical reconstructions of oceanic cloudiness, precipitation, and sea-surface temperature. The annual cycle is removed and data are normalized before the JEOF is performed. This JEOF gives the dominant joint variations in these reconstructed climate fields.26Seasonal Drought Prediction over the United StatesLi-Chuan Chen and Kingtse Mo

Objective:To predict meteorological drought over the contiguous United States using standardized precipitation index (SPI) based on precipitation forecasts from NCEP Climate Forecast System version 2.

Results:Six-month SPI (SPI6) is skillful out to 3-4 months.Skill is seasonally and regionally dependent.Seasonal forecasts became operational in April 2011. Products are available at http://www.cpc.ncep.noaa.gov/products/Drought/Figures/index/spi.fcst.gifIC: 15-16 October 2011 27Ecological Forecasting in Chesapeake BayChristopher BrownObjective

Generate short-term, ecological forecasts of Chesapeake Bay

ResultsDaily generate & disseminate forecasts of several noxious organisms: Sea nettles (jellyfish) Harmful algal blooms Water-borne pathogensEcological Forecast Example: Likelihood of encountering sea nettles, Chrysaora quinquecirrha, in Chesapeake Bay on August 17, 2007.

28Evaluation of Simulations of 20th Century PrecipitationLi RenSimulations of precipitation from one C20C , 11 CMIP5 historical simulations and 24 AR4 model runs are compared to 6 Reanalysis products (ERA-Interim, NCEP1, MERRA, CFSR, JRA25, 20thC) and GPCP and CMAP observations.

Aspects to be evaluated include: Long-term mean seasonal cycle over large domains (global, hemispheric, land/ocean, continental); The simulation of precipitation features associated with El Nio/Southern Oscillation (ENSO), NAO and PDO. Global mean annual mean: the observations (mean of GPCP and CMAP) are lower than the reanalyses (mean of 6 reanalyses) and the simulations; CMIP5 are closer to the reanalyses; C20C is the highest (Figure). Mean seasonal cycle: not much seasonal cycle on a global average; the N. and S. hemisphere out of phase; the simulations differ more from the observations in the ocean, particularly C20C; in the tropics GPCP is the lowest over the ocean (not shown).Red lines mark the medians; the bottom of the box marks the 25th percentile ; the top of the box marks the 75th percentile; lines extending from the top of the boxes mark the 98th percentile and from the bottom of the boxes mark the 2th percentile; the red plus signs mark the outliers (outside of the 98th percentile and 2th percentile).

29Terrestrial Ecosystem Functional TypesOmar Mller, Domingo Alcaraz-Segura, Hugo BerberyObjectivesTo develop measures of ecosystems from remote sensing information,

Assess their vulnerability and resilience to climate variations and extremes, and

Incorporate EFTs in models to investigate ecosystem-atmosphere feedbacks and coupling.

Ecosystem Functional Types based on three descriptors of the seasonal dynamics of the NDVI estimated from MODIS images for the period 2001-2009.

Results

Use of EFTs allows for time varying land surface states (usually fixed with traditional land cover types)

Use of EFTs improves the simulation of the drought and reduces the biases in regions of high P.

30As scientists, we are confronted with the challenge and pressure- of developing new products while ensuring they are of the quality and reliability needed to give truthful information.

31Regional climate modeling: Should one aim to improve also on the large scales? Can one do better than using the relaxation lateral boundary conditions?Fedor MesingerObjectives: Test: in verifications against analyses, is it possible to improve on large scales, compared to driver model, with no large scale nudging? Test: could more mathematically correct lateral boundary conditions (LBCs) do better than customary relaxation LBCs?Results:Verifying 26, and 6, Eta RCM 32-day forecasts against driver ECMWF ensemble forecasts, in two ways, gave answers to both test questions above:Yes, more often than not soExperiment design: Domain size: 12,000 x 7,550 km;Verifications: placing of strongest 250 hPa winds (as in precipitation verifications),and RMS wind difference forecast-ECMWF analyses (shown in the two plots to the right)

Red: ECMWF driver membersBlue: Eta RCM, Eta LBCsGreen: Eta RCM, relaxation 6 forecasts 26 forecasts Reference: Veljovic et al., 2010, Met. Zeitschrift, 19, 237-246. 32

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