Hydrological applications of remote sensing Atmospheric States and Fluxes-Insolation (VIS)

The key GEWEX objectives are: determination of the hydrological cycle and energy fluxes by global measurements of observable atmospheric and surface properties; modeling of the global hy

Hydrological applications of remote sensing: Atmospheric States and Fluxes-Insolation

(VIS)

Unique id:

hsa053-R T PinkerDepartment of MeteorologyUniversity of MarylandCollege Park, Maryland 20742Tel: 301-405-5380FAX: 314-9482e-mail: pinker@atmos.umd.edu

Keywords: insolation; surface radiative fluxes; shortwave radiation; satellite estimates

of surface radiation

Environmental satellites are now considered as promising tools to monitor climate and climate change by providing information on the terrestrial water and energy storage Radiative fluxes are the key forcing functions that determine the exchange of fluxes between the land and the atmosphere at the various temporal and spatial scales For several years now observations from satellites have been used to obtain information on atmospheric and surface radiative fluxes and presently, such information is being derived on a semi-operational basis Reviewed will be the context for the need of such information, methodologies used to obtain it, evaluation of the resulting fluxes, current status of data availability, examples ofapplications in hydrological studies and climate research, links to international activities, and future prospects

1.1 Need for information

Information on the spatial and temporal distribution of surface radiative fluxes is required for modeling the hydrologic cycle, for representing interactions and feedbacks

between the atmosphere and the terrestrial biosphere (Dickinson, 1986;

Henderson-Sellers, 1993; Prince et al., 1997), and for estimating global oceanic and terrestrial net

primary productivity (Goward, 1989; Running et al 1999; Platt, 1986) It is also needed for validating climate models (Garrat et al., 1993; Wild et al., 1995; Wielicki et al.,

2002); improving the understanding of transport of heat, moisture, and momentum across

the surface-atmosphere interface (Berbery et al., 1999; Baumgartner and Anderson, 1999; Sui et al., 2002); and for improving parameterizations (Chen et al., 1996) Surface

shortwave radiative fluxes are of interest due to their dominant role as forcing functions

of surface energy budgets (Wood et al., 1997; Wielicki et al., 1995; Mitchell et al., 2003;

Rodell et al., 2003) Studies on long-range weather are performed with the aid of

numerical weather prediction and general circulation models In order to use model results with confidence, there is a need to evaluate them at scales at which they are implemented Satellite observations are considered to be the only source of global scale information that could be used for model evaluation With the progress made in the use

of satellites to probe the atmosphere, the stage was set for focused efforts to advance satellite methods in climate research The Joint Scientific Committee (JSC) of the WCRPendorsed the Global Energy and Water Cycle Experiment (GEWEX) as a core activity (Chahine, 1992) The key GEWEX objectives are: determination of the hydrological cycle and energy fluxes by global measurements of observable atmospheric and surface properties; modeling of the global hydrological cycle; development of capabilities to predict variations of global and hydrological processes and water resources, and their response to environmental change; and foster the development of observational

techniques, data assimilation suitable for operational application to long-range weather forecasts, hydrology, and climate predictions Similar objectives play a key role in various national programs For instance, the Interagency Committee on Earth and

Environmental Sciences as well as the Intergovernmental Panel on Climate Change have identified clouds and the hydrological cycle to be of highest scientific priority in global

change research (Gates et al., 1999; Houghton et al., 2001) Clouds play a major role in

determining the net radiative balance, via optical properties and amount Objectives of

the NOAA Climate and Global Change Program include “improvement of our ability to observe, understand, predict, an respond to changes in the global environment”

Specific national research interests are linked to the GEWEX program in the framework of the continental scale GEWEX experiments such as the GEWEX

Continental-scale International Project GCIP/GAPP over the United States, LBA in the Amazon, BALTEX and GAME Methods to derive surface radiative fluxes have been made in the framework of most of these basin scale studies, specifically, over BALTEX

by Stuhlman, over LBA by Pereira and Ceballos, over GAME by Takamura et al

For example, the specific objectives of the GEWEX Continental-scale

International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP) (a

continuation of GCIP) (WCRP-67, 1992; NRC, 1998) are: determination of the

time/space variability of the hydrologic cycle and the energy budgets over the MississippiRiver Basin; development and validation of macro-scale hydrologic models; and,

utilization of existing and future satellite observations for achieving these objectives A summary of what was achieved can be found in Roads et al (2003)

The interest in surface radiative fluxes for the LBA is to have information needed for evaluating land surface parameterization, and to estimate the surface hydrological andenergy budgets over the Amazon Basin, provide otherwise missing surface data that are critical to provide adequate documentation of regional and continental energy and water cycles within the LBA; provide otherwise missing surface data that are critical to enhance

the understanding of mesoscale convective and/or land surface processes, provide

information to modelers that is critical to demonstrate the use of remotely sensed

hydrometeorological variables in connection with models of land surface fluxes

One of the major objectives of the LBA program is to improve the understanding of the hydrological cycle in this region, which serves as a major modulator of the hemispheric climate Major land use changes have been taking place in this region This year alone, the largest conflagration since 1925 in the Amazon Rain Forest in the region of Roraima occurred The blaze that has scorched an area of savanna of the size of Maryland and Delaware, spread to the forested area It started about two months ago by settlers who were clearing savanna regions for cropland, and the situation went out of control due ton the dry conditions, caused by the recent El Nino It is anticipated that spaceborne remote sensing capabilities will help to define the basin scale forcing functions, in order to determine how the basin functions as a regional entity Drastic changes have to be accounted for in the models that attempt to estimate the forcing functions by methods of remote sensing This is important because there is not enough confidence in the accuracy

of large scale models to project future change, and validation against remotely sensed data is important In particular, due to the lack of pertinent global change data from conventional sources The data to be produced will also enable to improve estimates of Net Primary Productivity in this region, namely, the growth of vegetation and CO2 exchange

1.2 Feasibility

In the last two decades, it has been demonstrated that radiative fluxes could be

derived from satellite observations with reasonable accuracy (Pinker et al., 1995; Frouin

and Pinker, 1995; Rossow and Zhang, 1995; Whitlock et al., 1995; Ohmura et al., 1998;

Gupta et al., 1998) Long-term satellite observations over large spatial scales are now

available for implementing inference schemes for deriving radiative fluxes (Schiffer and

Rossow, 1995; Rossow and Schiffer, 1999)

Methods to derive SW fluxes from satellite observations have been implemented

by several groups on different spatial and temporal scales Both METEOSAT, GOES, GMS and polar orbiting satelliteshave been used (Stuhlman et al., 1990; Pinker and Laszlo, 1992; Darnell et al., 1992; Gupta et al., 1992 Chou, 1994; Lee, 1993, Brison et al., 1994; Li et al., 1995; Rossow and Zhang, 1995) Global scale implementation was possible because of the availability of satellite data, which included nformation on the state of the atmosphere (Schiffer and Rossow, 1985) Valuable experience has been gained from the merging of the various global data sets (satellite observations; TOVS retrievals; snow cover) into coherent formats Two SW algorithms, developed at the NASA Langley Research Center (Darnell et al., 1992) and at the University of Maryland (Pinker and Laszlo, 1992) are currently used at NASA Langley Research Center in support of the WCRP/GEWEX activities

Attempts to implement retrieval methodologies operationally have been also successful For example, NOAA/NESDIS is supporting GCIP/GAPP activities by

developing new operational products from satellite observations [Leese, 1994; 1997] A new product on insolation is a collaborative effort between NOAA/NESDIS,

NOAA/National Centers for Environmental Prediction (NCEP), and the University of Maryland Shortwave upwelling and downwelling (0.2-4.0 µm) radiative fluxes at the surface and at the top of the atmosphere, as derived from The inferred shortwave

radiative fluxes include total and diffuse quantities (as appropriate), as well as spectral

components (e.g., the photosynthetically active radiation (PAR)) The interface between the satellite data and the inference models has been developed at NOAA/NESDIS

(Tarpley et al., 1996) NOAA/NCEP provides information on the state of the atmosphere

and surface conditions, as available from the analyzed output fields from the Eta model

(Rogers et al., 1996) The University of Maryland is involved in model development and modifications (Pinker and Laszlo, 1992a; 1992b; Pinker et al., 2003), sensitivity studies,

validation against ground observations, data archiving, and data distribution The Surface Radiation Budget (SRB) model is implemented at NOAA/NESDIS in real time on an hourly basis, for 0.5-degree targets for an area bounded by 66°-126° W longitude and 24°-54° N latitude belts For each target, at appropriate forecast times, selected data from the NCEP regional forecast model are delivered to the satellite data stream, as inputs to the SRB model This approach ensures timely and high quality information input to the satellite inference scheme In turn, the derived radiative fluxes help to diagnose the NCEPforecast model as to its ability to predict correctly radiative fluxes

2 Review of selected inference schemes

Methods spanning a wide range of complexity have been developed to derive surface radiative fluxes from satellite observations These have been reviewed in a series

of publications by Schmetz (1989; 1991; 1993) The emphasis has been on the critical evaluation of sensitivities to input parameters, as well as physical principles of the

methodologies In reviews that followed, the different parts of the spectrum were

discussed independently The current status of SW retrievals is summarized in Pinker et

al (1995) and Whitlock et al (1995) Methods to derive PAR are described in Frouin and

Pinker (1995) A summary of future satellite observations of relevance for SRB research are presented in Wielicki et al (1995)

2.2.1 Physical principles

In his discussion of physical principles that allow to derive SRB from satellite observations, Schmetz (1989) has stressed the importance of the close linear coupling between SW (0.2-4.0 mm) reflected radiance at the top of the atmosphere (albedo) and the surface irradiance Cloud extinction (transmittance) and albedo are linearly related since atmospheric constituents do not emit radiation at solar wavelength There is a dependence on solar zenith angle; gaseous and aerosol absorption and scattering; surface reflectivity; and clouds

2.2.2 Current status

A modified version of the GEWEX SRB algorithm (Pinker and Laszlo, 1992a; Whitlock et al., 1995; Ohmura et al., 1998) (Version 1.1), developed at the University of

Maryland is used The algorithm estimates downward and upward fluxes both at the top

of the atmosphere (TOA) and at the surface A diagram of the flux retrieval process is

presented in Figure 2 The TOA downward flux (Ftd) is calculated from the

extraterrestrial solar spectrum by accounting for the variation in sun-earth distance andthe position of the sun in the sky relative to the local vertical (solar zenith angle) The

downward flux at the surface (Fsd) is obtained by determining what fraction of Ftd

reaches the surface as the radiation is transferred through the atmosphere This fraction,

which is referred to as the flux transmittance (T), depends on the composition of the

atmosphere (e.g., amount of water vapor and ozone, optical thickness of cloud and

aerosol), on the length of the path the radiation travels through the atmosphere

(determined by the solar zenith angle), and to a lesser degree, on the albedo of the

surface Once T is known, the surface downward flux is obtained as Fsd = T Ftd The algorithm estimates T from the satellite derived TOA albedo (as described below) This is

possible because for a given atmosphere and surface, the TOA albedo and the flux

transmittance are uniquely related to each other Once Fsd is known, the upward flux at the surface (Fsu) is calculated as Fsu = As Fsd, where As is the surface albedo

Similarly, the flux reflected to space by the earth-atmosphere system (TOA upward flux,

Ftu) is obtained from the product of Ftd and the TOA albedo (At), namely, Ftu = At Ftd.

T is determined from a comparison of modeled values of the shortwave

(0.2-4.0 µm) TOA albedos to the shortwave TOA albedo obtained from the satellitemeasurement, and the transmittance corresponding to the modeled TOA albedo thatmatches the satellite-derived value is selected For practical reasons, the pairs of albedosand transmittances are calculated for atmospheres with a non-reflecting lower boundary The surface reflection is added in a separate step The modeled TOA albedos and the corresponding transmittances are calculated at five spectral intervals (0.2-0.4, 0.4-0.5, 0.5-0.6, 0.6-0.7 and 0.7-4.0 µm) for discrete values of the solar zenith angle, amount of water vapor and ozone, aerosol and cloud optical thickness, using the delta-Eddington

radiative transfer method described in Joseph et al (1976) Radiative properties of aerosols and clouds are taken from the Standard Radiation Atmospheres (WCP-55, 1983) and from Stephens et al (1984), respectively Absorption by ozone and water vapor are parameterized following Lacis and Hansen (1974) The albedo-transmittance pairs are

made available in a lookup table for the algorithm separately for clear and cloudy

atmospheres, and the flux transmittances for clear and cloudy skies are determined bymatching the satellite-observed clear and cloudy shortwave TOA albedos, respectively For a given solar zenith angle, surface albedo and amount of ozone and water vapor, thematching process involves the adjustment of the aerosol optical depth for clear sky andthat of the cloud optical depth for cloudy sky For GCIP/GAPP, the satellite-observed TOA shortwave albedo is obtained from the visible (0.55-0.75 µm) radiance measured bythe imager instrument onboard the GOES-8 satellite through spectral and angular

transformations (Zhou et al., 1996) (for details see section 3.2) In deriving the fluxes,

first the surface albedo is estimated from the “clearest” shortwave TOA albedo observed over a number of days (clear-sky composite albedo), and then corrected for Rayleigh scattering, aerosol extinction, and absorption by ozone and water vapor In this step, the

amount of aerosol is specified according to the Standard Radiation Atmospheres

(WCP-55, 1983) For GCIP/GAPP, the column amount of ozone is taken from the McClatchy

atmospheres (Kneizys et al., 1988) as a function of latitude and season, while water vapor

is from the NCEP Eta model Next, albedo-transmittance pairs are selected from the lookup table according to the solar zenith angle, water vapor and ozone amount, and are combined with the surface albedo to yield shortwave TOA albedos One set of pairs is for varying values of aerosol optical depth (clear atmosphere), and the other is for

varying values of cloud optical depth (cloudy atmosphere) Finally, the shortwave

albedos derived from the instantaneous satellite-observed clear-sky and cloudy-sky radiances are matched with the clear and cloudy sets of albedo-transmittance pairs, and clear-sky and cloudy-sky transmittances, and from these, clear-sky and cloud-sky fluxes are obtained The clear-sky and cloudysky fluxes are then weighted according to the

cloud cover (defined as the ratio of number of cloudy pixels to the total number of pixels)

to get the all-sky fluxes Incorporated was the new parameterization of water vapor absorption, following Ramaswamy and Freidenreich (1992) The implementation of the model requires preprocessing of the satellite data and separation of clear and cloudy

radiances This is performed at NOAA/NESDIS (Tarpley et al., 1996), as described in

section 4 The various elements of the GEWEX/SRB algorithm have been tested in a number of different ways The radiative transfer component has been evaluated in the framework of the Intercomparison of Radiation Codes in Climate Models (ICRCCM) The results were found to be in good agreement with those from high-resolution

radiative-transfer models (Fouquart et al., 1991) The differences in atmospheric

absorption, when compared to high-resolution computations (namely, standard deviations

of differences expressed as percentage of the average absorption for the reference (high resolution) model) are about +/- 2 % and +/- 7 % for the clear and cloudy cases,

respectively Surface down-flux estimates have been compared with values measured at several locations and in the framework of various activities, such as the Satellite

Algorithm Intercomparison sponsored by WCRP and NASA (Whitlock et al., 1995),

reporting agreement with ground observations within 10 Wm-2 on a monthly time scale

In the SRB algorithm, the fluxes are calculated in the spectral intervals of 0.2-0.4,

0.4-0.5, 0.5-0.6, 0.6-0.7 and 0.7-4.0 µm Thus, it is possible to obtain fluxes at spectral intervals known to be of significance (e.g., photosynthetically active radiation) This is important, because current GCMs are run in a mode that separates shortwave fluxes at

0.7 µm (Roesch et al., 2002), to allow incorporation of newly derived satellite based

parameters, such as fractional vegetation cover, derived from the Normal Difference

Vegetation Index (NDVI), and for improving parameterizations of surface/atmosphere interactions (Gallo and Huang, 1998; Goward and Huemmrich, 1992; Townshend and

Justice, 1995; Gutman et al., 1995) Moreover, shortwave fluxes are separated into direct

and diffuse components, which is of interest for improved modeling of radiative

interaction with vegetation and oceans Other parameters that are derived include

clearsky and all-sky albedos at the top of the atmosphere and at the surface, and aerosol and cloud optical depths The shortwave and spectral fluxes are computed separately for clear and all-sky conditions, thus making it possible to derive information on the radiative

effects of clouds, known as "cloud radiative forcing" (Ramanathan et al., 1989)

3 CURRENT STATUS OFG DATA AVAILABILITY

Under the joint NOAA/NASA PATHFINDER activity, uniform, long term data sets from observations made from numerous satellites, are being prepared into homogeneous time series Some of these data are processed into reduced resolution, multi-satellite, global coverage information, and are known as ISCCP D1 data Due to the

representation of the diurnal cycle, and the long-term availability, they are of particular interest to scientists working on land-atmosphere and ocean-atmosphere interactions and hydrologic modeling The data can also be considered as precursors to data streams anticipated under the EOS missions, such as the Clouds and the Earth's Radiant Energy System (CERES) We have produced eleven years of surface and top of the atmosphere short-wave radiative fluxes using as inputs the ISCCP D1 data and Version 2.1 of the GEWEX/Surface Radiation Budget (SRB) model They were prepared for distribution

on a CD-ROM available from srb@meto.umd.edu Described will be the content of this

information, as well, as preliminary statistical results, and examples of data use in climateresearch.

In this CD-ROM, global scale daily and monthly mean values of Surface and Top

of the Atmosphere Shortwave Radiation Budget (SRB) Parameters, as produced at the University of Maryland (UMD), are provided for a period of 133 months The derived values are based on satellite observations and on ancillary data, as available from the Global Energy and Water Cycle Experiment (GEWEX) (WCRP-67, 1992) ISCCP D1 product (Rossow and Schiffer, 1991), at a nominal resolution of 2.5 degrees The

inference scheme used to derive these parameters is Version 2.1 of the

UMD/GEWEX/SRB algorithm, as described in Pinker and Laszlo (1992), Whitlock et al (1995), Pinker et al (1995) and Laszlo et al (1997a) The ISCCP D1 product is an improved version of the ISCCP C1 product (Schiffer and Rossow, 1985) Improvements are related to new cloud screening methodology used to produce the D1 data version, resulting in better cloud detection over snow cover, in particular, in the polar regions Previous GEWEX/SRB products are based on ISCCP C1 observations, as provided in:

O First WCRP Surface Radiation Budget Global Data Sets, Shortwave Radiation

Parameters March 1985-December 1988, NASA Earth Observing System

Distributed Active Archive Center, NASA Langley Research Center, Hampton,

VA (Whitlock et al., 1995), at monthly time scale

O Global Data Sets for Land-Atmosphere Models, ISLSCP Initiative 1: 1987-1988, Volume 1-5, NASA Goddard DAAC Science Data Series, at three hourly intervals (Meeson et al., 1998); and the

O Global Ecosystem Database, Disk B, National Environmental Satellite, Data, and Information Center National Geophysical Data Center, Boulder, Colorado, November

1997, which provides information at three hourly intervals, monthly averaged on Photosynthetically Active Radiation, for the period July 1983-July 1988

1 NASA Langley produced a five year CD-ROM entitled: "First WCRP Surface Radiation Budget Data Sets, Shortwave Radiation Parameters, March 1985 December 1988", available from the Langley DAAC

2 NASA Goddard produced a CD-ROM with two years of data of interest to ISLSCP activities (Meeson and Sellers, 1995; Meeson et al., 995) entitled: "Global Data Sets for Land-Atmosphere Models, ISLSCP Initiative 1: 1987-1988, Volumes 1-5", that included our surface shortwave and PAR data, at three hourly intervals

3 The National Geophysical Data Center in Boulder Colorado, has produced a ROM to support ecological modeling which includes a five year data set of our PAR data

CD-It is entitled: Global Ecosystems Database Disc-B, NOAA/NGDC Solid Earth

Geophysics Division (J J Kineman, ed., 1997)

4 The "real time" GCIP data will be distributed by the University of Maryland via a web site A sample that covers about half a year is now available at:

http://www.meto.umd.edu/~srbExamples of surface shortwave radiation fields at various time scales, as well asexamples of by-products, such as net absorbed radiation at the surface and within the

atmosphere, cloud amounts and cloud optical depths, are illustrated in Figure 1.

Information on instantaneous, daily, monthly mean surface and TOA shortwave andphotosynthetically active radiative fluxes are provided at the University of MarylandWorld Wide Web site almost in real time

The first version of the experimental real time product has been available since January 1996 All the input and output parameters, produced in support of GCIP/GAPP (currently, a total of 71), are stored at the University of Maryland, where the SRB data are evaluated against ground observations, partially quality controlled, and prepared for distribution via the World Wide Web and an anonymous ftp site, as described at:

http://www.atmos.umd.edu/~srb/gcip/webgcip.htm

Four types of information are archived:

• satellite based information, used to drive the model;

• auxiliary data used to drive the model;

• Eta model output products relevant for hydrologic modeling;

independently derived satellite products

Examples of surface shortwave radiation fields at various time scales, as well as

examples of by-products, such as net absorbed radiation at the surface and within the atmosphere, cloud amounts and cloud optical depths, are illustrated in Figure 1

Information on instantaneous, daily, monthly mean surface and TOA shortwave and photosynthetically active radiative fluxes are provided at the University of Maryland World Wide Web site almost in real time

In support of the World Climate Research Program (WCRP) GEWEX

Continental-scale International Project (GCIP) and the GEWEX Americas PredictionProject (GAPP), real time estimates of shortwave radiative fluxes, both at the surface and

at the top of the atmosphere, are being produced operationally by the National Oceanicand Atmospheric Administration (NOAA)/National Environmental Satellite Data andInformation Service (NESDIS), using observations from the GOES-8 Imager The

inference scheme has been developed at the Department of Meteorology, University ofMaryland (UMD), and the atmospheric and surface model input parameters are producedand provided by the NOAA/National Centers for Environmental Prediction (NCEP) Theradiative fluxes are being evaluated on hourly, daily, and monthly time scales, usingobservations at about fifty stations The satellite estimates have been found to be withinacceptable limits during snow-free periods, but the difficulty to detect clouds over snowaffects the accuracy during the winter season In what follows, this activity is discussed,and evaluation results of the derived fluxes against ground observations for time periods

of one to two years are presented The GEWEX Americas Prediction Project (GAPP) is acontinuation of GCIP, having the ultimate objective to develop capabilities for predicting

variations in water resources, on time scales up to seasonal and interannual [NRC, 1998].

NOAA/NESDIS is supporting GCIP/GAPP activities by developing new operational

products from satellite observations (Leese, 1994; 1997) This is a collaborative effort

between NOAA/NESDIS, NOAA/National Centers for Environmental Prediction

(NCEP), and the University of Maryland Shortwave upwelling and downwelling 4.0 m) radiative fluxes at the surface and at the top of the atmosphere, as derived fromGOES observations, are part of this product The inferred shortwave radiative fluxesinclude total and diffuse quantities (as appropriate), as well as spectral components (e.g.,the photosynthetically active radiation (PAR)) The interface between the satellite data

(0.2-and the inference models has been developed at NOAA/NESDIS (Tarpley et al., 1996).

NOAA/NCEP provides information on the state of the atmosphere and surface

conditions, as available from the analyzed output fields from the Eta model (Rogers et

al., 1996) The University of Maryland is involved in model development and

modifications (Pinker and Laszlo, 1992a; 1992b; Pinker et al., 2002), sensitivity studies,

validation against ground observations, data archiving, and data distribution The SurfaceRadiation Budget (SRB) model is implemented at NOAA/NESDIS in real time on anhourly basis, for 0.5-degree targets for an area bounded by 66°-126° W longitude and24°-54° N latitude belts For each target, at appropriate forecast times, selected data fromthe NCEP regional forecast model are delivered to the satellite data stream, as inputs tothe SRB model This approach ensures timely and high quality information input to thesatellite inference scheme In turn, the derived radiative fluxes help to diagnose theNCEP forecast model as to its ability to predict correctly radiative fluxes

Parameters provided

The following parameters are provided both on daily and monthly time scales:

 Shortwave surface downward flux

 Shortwave surface upward flux

 Visible surface downward flux (Photosynthetically Active Radiation (PAR))

 Visible surface upward flux

 Shortwave top of the atmosphere net flux (down-up)

These parameters represent the most frequently requested information on radiative fluxes,and are only a subset of parameters produced by the inference scheme Moreover, an extensive evaluation was conducted on these parameters against ground truth at the surface, and against ERBE observations at the top of the atmosphere Additional

parameters will be provided at a later stage, after their quality evaluation is completed The monthly mean fields of all five parameters are also available at:

by independent investigators and within the framework of the North American Land Data

Assimilation System (N-LDAS) activity (Lou et al., 2003; Cosgrove et al., 2003a)

The ground truth as available from the Swiss Federal Institute of Technology, Global Energy Balance Archive (GEBA) (Ohmura et al., 1998), and the NOAA Climate

Monitoring and Diagnostics Laboratory (CMDL), as compiled at the NASA Langley Data Center, was used These parameters represent the most frequently requested

information on radiative fluxes, and are only a subset of parameters produced by the inference scheme Moreover, an extensive evaluation was conducted on these parametersagainst ground truth at the surface, and against ERBE observations at the top of the atmosphere Additional parameters will be provided at a later stage, after their

quality evaluation is completed

The ARM observations of shortwave fluxes are obtained from the U.S SouthernGreat Plain (SGP) central facility (36.605 N, 97.485 W), operated according to thespecifications of the Baseline Surface Radiation Network (BSRN) (DeLuisi, 1991) TheCASES observations of SW fluxes were obtained from eight stations within the area of(37.275-37.593 N, 96.555-97.140 W) Both ARM and CASES used broadbandinstruments of the Eppley Precision Spectral Pyranometer type.

5 Applications

5.1 Link to hydrological modeling

The importance of land surface processes for climate and weather modeling has been recognized Many current general circulation and climate models are coupled with

Land Surface Schemes For instance, under the Land Data Assimilation Systems (LDAS)

activity, developed are LDAS both for the North American (NLDAS) and global

(GLDAS) scale It is hoped that these activities will lead to more accurate reanalysis and forecast simulations by numerical weather prediction (NWP) models Specifically, the system will reduce the errors in the storage of soil moisture and energy which are often present in NWP models and which degrade the accuracy of forecasts The systems are currently forced by terrestrial (NLDAS) or space based (GLDAS) precipitation data, space-based radiation data and numerical model outputs Radiative fluxes as derived from satellite observations over the Eta model domain since 1996, played an important role in the LDAS activity The radiation scheme is now operational at NOAA/NESDIS, and improvements to methodologies are in progress under grant NA06GP0404 from NOAA OGP, which is about to expire It is the objective of this proposal to test the

transferability of the methodology developed under GCIP to other regions in the

framework of CEOP

Several issues in climate research are related to the Atlantic Ocean Examples include theTropical Atlantic Variability (TAV), the North Atlantic Oscillation (NAO), their

interrelationship at different time scales, their control of air-sea interactions, and

influence on sea surface temperatures and precipitation Information on the oceanic energy balance is needed to address these issues Radiative fluxes absorbed at the ocean surface affect the ocean temperatures and the entire surface energy budget at shorter time scales In the past, latent and sensible heat fluxes have been estimated using bulk

formulas (Liu et al., 1979; Geernaert, 1990), while more recently, multi-sensor techniquesusing microwave radiometers and scatterometers have yielded promising results

(Bentamy et al., 2001a,b; Liu et al., 2001) It is now timely to develop an approach to theoceanic energy balance where estimates of all relevant fluxes, namely, radiative, latent, and sensible, are made by methods of remote sensing Present limitations for

implementing such an approach are largely due to the lack of simultaneous observations

of all the required parameters The objective of this presentation is to review activity thatshould lead to an all satellite based approach initially, with existing observations, and with future satellite observations

An essential component of land surface modeling studies is the forcing data used

to drive the participating land surface models (LSMs) No matter how sophisticated their depiction of land surface processes, or how accurate their boundary and initial conditions are, such models will not produce realistic results if the forcing data is not accurate

LSMs depend upon such externally supplied quantities as precipitation, radiation,

temperature, wind, humidity and pressure to forecast land surface states, and errors in any

of these quantities can greatly impact simulations of soil moisture, runoff, snow pack and latent and sensible heat fluxes Each of these forcing quantities can be supplied by atmospheric Numerical Weather Prediction (NWP) models; however, such models are subject to internal model biases and errors in parameterizations that may negatively impact the quality of their output As such, a more robust approach is to make

use of as much observation-based forcing data as possible This approach is especially important for offline Land Data Assimilation Systems (LDAS) Such systems seek to produce accurate simulations of land surface states by making use of observational data and isolating land surface modeling systems from the biases inherent in internally cycled NWP modeling systems With this in mind, the North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 1999; Mitchell et al., 2003, this issue) has sought to construct quality controlled, spatially and temporally consistent, real-time and retrospective forcing data sets from the best available observations and model output to support its multi-LSM modeling activities NLDAS and Atmospheric Administration (NOAA) National Centers for Environmental Prediction Environmental Modeling Center(NCEP/EMC), National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), National Weather Service Office of Hydrologic Development (NWS/OHD), National Environmental Satellite, Data, and Information Service

Office of Research and Applications (NESDIS/ORA), Princeton University, Rutgers University, the University of Washington and the University of Maryland NLDAS utilizes the real-time and retrospective forcing data sets mentioned above to execute the

Noah Mosaic [Koster and Suarez, 1992], VIC [Liang et al., 1996] and Sacramento

(Burnash et al.,1973) LSMs These forcing data sets feature hourly temporal resolution and 1/8th degree spatial resolution, and have been extensively quality controlled and validated [Luo et al., 2003, this issue; Pinker et al., 2003, this issue] While these forcing data sets are based on a backbone of operational NCEP data assimilation fields (derived from merging observations with model fields) they are supplemented with extensive observation-based precipitation and shortwave radiation data—two forcing quantities thatcharacteristically suffer significant biases in NWP assimilation and prediction systems and which greatly influence land surface simulations

The accuracy of forcing data greatly impacts the ability of Land Surface Models (LSMs)

to produce realistic simulations of land surface processes With this in mind, the institutional North American Land Data Assimilation System (NLDAS) project has produced retrospective (1996-2002) and real-time (1999-Present) data sets to support its LSM modeling activities Featuring 1/8th degree spatial resolution, hourly temporal resolution, 9 primary forcing fields, and 6 secondary validation/model development fields, each data set is based on a backbone of Eta Data Assimilation System (EDAS)/Eta data, and is supplemented with observation-based precipitation and radiation data Hourlyobservation-based precipitation data are derived from a combination of daily NCEP Climate Prediction Center (CPC) gauge-based precipitation analyses and hourly National Weather Service Doppler Radar-based (WSR-88D) precipitation analyses, wherein the hourly radar-based analyses are used to temporally disaggregate the daily CPC analyses NLDAS observation-based shortwave values are derived from Geostationary

multi-Operational Environmental Satellite (GOES) radiation data processed at the University ofMaryland and at the National Environmental Satellite, Data, and Information Service (NESDIS) Extensive quality control and validation efforts have been conducted on the NLDAS forcing data sets, and favorable comparisons have taken place with Oklahoma Mesonet, Atmospheric Radiation Measurement / Cloud and Radiation Testbed

(ARM/CART), and Surface Radiation (SURFRAD) observation data The real-time forcing data set is constantly evolving to make use of the latest advances in forcing-related data sets, and all of the real-time and retrospective data are available online at ldas.gsfc.nasa.gov for visualization and downloading in both full and subset forms

The hourly NLDAS retrospective forcing data set features a 1/8th degree spatialresolution, is valid over the central North American NLDAS domain illustrated in Figure

1, and was produced in collaboration with NLDAS project members at NASA GSFC The data set extends from 1996 through 2002, and an October 1st 1996 to September 30th 1999 subset serves as the basis for several companion NLDAS papers in this JGR issue The retrospective forcing data set was constructed especially for the purposes of 1)executing the participating NLDAS LSMs for periods that overlap with special validationdata sets, such as soil moisture [Robock et al., 2003, this issue], 2) taking advantage of input forcing data that is not available in real-time, especially additional gauge

observations of precipitation [Shi et al., 2003, this issue], and 3) making use of

observation-based precipitation and radiation data sets that pass through additional

quality control checks which are not available to their real-time counterparts Generation

of the retrospective forcing data was made possible by the extensive Global Energy and

Water Cycle Experiment Continental-Scale International Project (GCIP) sponsored archives of gauge-, radar-, and model-based data produced by EMC and CPC of NCEP atNCAR, and of GOES-based fields produced by the University of Maryland.

A Global Land Data Assimilation System (GLDAS) has been developed Its purpose is to ingest satellite- and ground-based observational data products, using

advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes GLDAS is unique in that it is an

uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation based data, runs globally at high resolution (0.25°), and produces results in near-real time (typically within 48 hours of the present) GLDAS is also a test bed for innovative modeling and assimilation capabilities A vegetation-based “tiling” approach is used to simulate sub-grid scale variability, with a 1 km global vegetation dataset as its basis Soil and elevation parameters are based on high resolution

global datasets Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will

promote various hydrometeorological studies and applications The 2001-forward

GLDAS archive of modeled and observed, global, surface meteorological data, parametermaps, and output is publicly available

Land surface temperature and wetness conditions affect and are affected by numerous

climatological, meteorological, ecological, and geophysical phenomena Therefore, accurate, high resolution estimates of terrestrial water and energy storages are valuable for predicting climate change, weather, biological and agricultural productivity, and flooding, and for performing a wide array of studies in the broader biogeosciences In particular, terrestrial stores of energy and water modulate fluxes between the land and atmosphere and exhibit persistence on diurnal, seasonal, and interannual timescales Furthermore, because soil moisture, temperature, and snow are integrated states, biases inland surface forcing data and parameterizations accumulate as errors in the

representations of these states in operational numerical weather forecast and climate models and their associated coupled data assimilation systems That leads

to incorrect surface water and energy partitioning, and, hence, inaccurate predictions Reinitialization of land surface states would mollify this problem if the land surface fieldswere reliable and available globally, at high spatial resolution, and in near real-time

A Global Land Data Assimilation System (GLDAS) has been developed jointly by scientists at NASA’s Goddard Space Flight Center (GSFC) and NOAA’s National Centersfor Environmental Prediction (NCEP) in order to produce such fields GLDAS makes use of the new generation of ground- and space-based observation systems, which

provide data to constrain the modeled land surface states Constraints are applied in two ways First, by forcing the land surface models (LSMs) with observation-based

meteorological fields, biases in atmospheric model-based forcing can be avoided

Second, by employing data assimilation techniques, observations of land surface states can be used to curb unrealistic model states

Through innovation and an ever-improving conceptualization of the physics underlyingEarth system processes, LSMs have continued to evolve and to display improved ability

to simulate complex phenomena Concurrently, increases in computing power and

affordability are allowing global simulations to be run more routinely and with less processing time, at spatial resolutions that could only be simulated using supercomputers five years ago GLDAS harnesses this low-cost computing power to integrate

observation-based data products from multiple sources within a sophisticated, global, high resolution land surface modeling framework What makes GLDAS unique is the union of all of these qualities: it is a global, high resolution, offline (uncoupled to the atmosphere) terrestrial modeling system which incorporates satellite and ground based observations in order to produce optimal fields of land surface states and fluxes in near-real time This article describes the major aspects of GLDAS and includes a

sample of the output products Subsequent scientific papers will present the results of several studies (now in various stages of completion) which are focusing on the data assimilation, validation, weather and climate model initialization, and other aspects of theproject, in more detail than could be included in a single article

2 Background

a Modeling of the land surface

Spurred by advances in the understanding of soil-water dynamics, plant physiology, micrometeorology, and the controls on atmosphere-biosphere-hydrosphere interactions, several LSMs have been developed in the past two decades with the goal of realistically simulating the transfer of mass, energy, and momentum between the soil and vegetation surfaces and the atmosphere Currently, GLDAS drives three land surface models:

Mosaic, Noah, and the Community Land Model (CLM) Additional models are slated for future incorporation, including the Variable Infiltration Capacity model (VIC; Liang et al.1994) and the Catchment Land Surface Model (Koster et al 2000) For a comparison of these and other LSMs, see results from the Project for Intercomparison of Land Surface Parameterization Schemes (Henderson- Sellers et al 1995; Bowling et al 2003) and the Global Soil Wetness Project (Dirmeyer et al 1999)

The multi-institution North American Land Data Assimilation System (NLDAS):Utilizing multiple GCIP products and partners in a continental distributed hydrologicalmodeling system Improving weather and seasonal climate prediction by dynamicalmodels requires multidisciplinary advances in providing reliable initial states for theatmosphere, ocean and land components of the earth system For two decades, advances

in providing atmospheric initial states via 4-dimensional data assimilation (4DDA) havepaved the way for emerging 4DDA systems for the ocean and land The backbone of any4DDA system is the geophysical model whose execution provides temporally andspatially continuous background states, into which generally discontinuous observationsare assimilated from various observing platforms (in situ, satellite, radar) For example,present space-based microwave estimates of soil moisture sense only the top 1-5 cm ofsoil, far short of the root-zone depths needed for land-state initialization

Thus, a land data assimilation system (LDAS) is needed to blend sparse landobservations with the background fields of a land surface model (LSM) The accuracy ofthe LSM background field (and companion surface and sub-surface water/energy fluxes)

is crucial to LDAS viability The chief objective of the NLDAS study here is to generate

and validate, over a 3-year period over the CONUS domain, the background land statesand surface fluxes of four LSMs: Noah, Mosaic, VIC, and Sacramento – denoted SAC(hereafter, all acronyms are defined in Appendix) Future NLDAS papers will addressactual data assimilation experiments using such methods as adjoint models and Kalmanfiltering As one step to assimilation of satellite LST, this paper assesses geostationarysatellite-derived LST and uses it to validate NLDAS LST.

It is instructive to consider the infancy of realtime large-scale land 4DDA Globalatmospheric 4DDA has been a mainstay of operational NWP centers since the late1970’s Realtime ocean 4DDA on large-scale ocean basins followed in the mid to late

1980’s (Ji et al., 1994) on the heels of the TOGA program Yet until the mid 1990’s,

initiatives in realtime continental or global land 4DDA were virtually non-existent Thefirst viable examples of realtime land 4DDA on continental or global scales were the

coupled land-atmosphere 4DDA systems at major NWP centers such as NCEP (Kalnay et al., 1996) and the European Center for Medium Range Weather Forecasting (Gibson et al., 1997) Such coupled land-atmosphere 4DDA systems (including global reanalysis)

often yield significant errors and drift in soil moisture/temperature and surfaceenergy/water fluxes, owing to substantial biases in the surface forcing from the parentatmospheric models To constrain such errors and drift, coupled land-atmosphere 4DDAsystems temporally nudge the soil moisture by such means as 1) a climatology of soil

moisture (Kalnay et al., 1996), 2) differences between the observed and 4DDA background fields of precipitation (Kanamitsu et al., 2002) or 3) screen-level air temperature and dew point (Douville et al., 2000) Such nudging methods, however, do

not reduce the main error source, namely large bias in the land surface forcing (especiallyprecipitation and solar insolation) of the parent atmospheric model.

Substantial biases in atmospheric model surface forcing also plague ocean 4DDA

To improve these surface fluxes, "flux corrections" are applied in ocean 4DDA (Ji et al.,

1994) NLDAS here also applies surface flux corrections As a pathfinder for this, the

GEWEX Global Soil Wetness Project (GSWP, Dirmeyer et al., 1999) retrospectively

demonstrated the viability of using non-model, observation-based precipitation analysesand non-model, satellite-based surface insolation fields (with all other surface forcingfrom atmospheric 4DDA) to drive uncoupled, land-surface models over a global domain

However, the monthly satellite retrievals of precipitation and insolation used in GSWP are

not conducive to the daily/weekly updates of land states needed to initialize operationalprediction models Hence, the NLDAS project set and achieved the following keyobjectives: 1) develop and execute the first realtime operational prototype of acontinental-scale uncoupled land 4DDA backbone (continuously cycled land-modelstates) executed daily at NCEP using realtime streams of hourly to daily data, and 2) acompanion retrospective mode for research The NLDAS generates hourly surfaceforcing (using model-independent, observation-based precipitation and insolation fields)that drives four LSMs running in parallel to produce hourly output on a 1/8° grid over aCONUS domain

The retrospective NLDAS spans October 1996 to September 1999 and uses supported archives of NOAA operational data streams NLDAS thus provides a land 4DDA counterpart from the GEWEX community to complement the ocean 4DDA thruststhat followed TOGA Moreover, a core objective of GCIP is the infusion of GCIP

GCIP-research into NOAA operational practice The NLDAS partnership of operational and research investigators in both meteorology and hydrology is a flagship of GCIP success

in such infusion

The studies by Cosgrove et al (2003a) (CL-N), Pinker et al (2003) (PT-N), and Luo

et al (2003) (LR-N) summarized below describe the data sources, generation and

validation of NLDAS forcing, produced in realtime and retrospectively on the NLDASgrid Of the 16 fields in each forcing file (Table 2), the nine fields required by Noah,Mosaic, and VIC are primary: U/V 10-m wind components, 2-m air temperature andspecific humidity, surface pressure, downward longwave and shortwave radiation, and

convective and total precipitation SAC requires only total precipitation (P), air temperature and potential evaporation (PE) In NLDAS, SAC uses the PE computed in

the Noah LSM Mosaic alone requires convective precipitation

The chief source of NLDAS forcing is NCEP's Eta model-based Data Assimilation

System (EDAS, Rogers et al., 1995), a continuously cycled N American 4DDA system.

It utilizes 3-hourly analysis-forecast cycles to derive atmospheric states by assimilatingmany types of observations, including station observations of surface pressure andscreen-level atmospheric temperature, humidity and U and V wind components EDAS3-hourly fields of the latter five variables plus surface downward shortwave andlongwave radiation and total and convective precipitation are provided on a 40-km grid toNLDAS forcing software, which interpolates the fields spatially to the NLDAS grid andtemporally to one hour Last, to account for NLDAS vs EDAS surface-elevationdifferences, a terrain-height adjustment is applied to the air temperature and surface