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NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗
NASA NTRS2017

Status and Plans for Reanalysis at NASA/GMAO

Reanalysis plays a critical role in GMAOs goal to enhance NASA's program of Earth observations, providing vital data sets for climate research and the development of future missions. As the breadth of NASAs observations expands to include multiple components of the Earth system, so does the need to assimilate observations from currently uncoupled components of the system in a more physically consistent manner. GMAOs most recent reanalysis of the satellite era, MERRA-2, has completed the period 1980-present, and is now running as a continuing global climate analysis with two- to three-week latency. MERRA-2 assimilates meteorological and aerosol observations as a weakly coupled assimilation system as a first step toward GMAOs longer term goal of developing an integrated Earth system analysis (IESA) capability that will couple assimilation systems for the atmosphere, ocean, land and chemistry. The GMAO strategy is to progress incrementally toward an IESA through an evolving combination of coupled systems and offline component reanalyses driven by, for example, MERRA-2 atmospheric forcing. Most recently, the GMAO has implemented a weakly coupled assimilation scheme for analyzing ocean skin temperature within the existing atmospheric analysis. The scheme uses background fields from a near-surface ocean diurnal layer model to assimilate surface-sensitive radiances plus in-situ observations along with all other observations in the atmospheric assimilation system. In addition, MERRA-2-driven simulations of the ocean (plus sea ice) and atmospheric chemistry (for the EOS period) are currently underway, as is the development of a coupled atmosphere-ocean assimilation system. This talk will describe the status of these ongoing efforts and the planned steps toward an IESA capability for climate research.

MERRA-2↗
NASA NTRS1992

Assimilation of satellite color observations in a coupled ocean GCM-ecosystem model

Monthly average coastal zone color scanner (CZCS) estimates of chlorophyll concentration were assimilated into an ocean global circulation model(GCM) containing a simple model of the pelagic ecosystem. The assimilation was performed in the simplest possible manner, to allow the assessment of whether there were major problems with the ecosystem model or with the assimilation procedure. The current ecosystem model performed well in some regions, but failed in others to assimilate chlorophyll estimates without disrupting important ecosystem properties. This experiment gave insight into those properties of the ecosystem model that must be changed to allow data assimilation to be generally successful, while raising other important issues about the assimilation procedure.

Sarmiento, Jorge L.↗
NASA NTRS2017

Observations, Modeling, and Predictions of Solar Activity From the Deep Interior to the Corona

Solar activity is a manifestation of magnetic self-organization processes that involve complex dynamical coupling of various layers of the Sun acting over a broad range of spatial and temporal scales. Synergy of observational, theoretical, and modeling efforts is key to understanding solar activity variation, dynamics, and evolution and to developing reliable physics-based forecasts of long-term solar cycles and short-term activity manifestations, “seasons of solar activity”, such as periods of enhanced flaring and CME activity. The session welcomes observers, modelers, and theoreticians to share their results and ideas and to discuss current challenges, development of emerging fields (such as data assimilation), and the analysis of historical and modern observational data using theoretical modeling, interpretations, and predictions.

Deep Interior↗
NASA NTRS1994

Empirical convection models for northward IMF

It is clear that polar cap convection during times of northward Interplanetary Magnetic Field (IMF) is more structured and of lower mean speed than at times of southward IMF. This, coupled with the fact that the polar cap is smaller, means that empirical models are more difficult to construct with certainty. It is also clear that sunward flow deep in the polar cap is often observed, but its connection with the rest of the flow pattern is controversial. At present, empirical models are of three types: 'statistical' models wherein data from different days but with similar IMF conditions are averaged together; 'pattern recognition' models, which are built up by examining individually hundreds of passes to derive a 'typical' pattern which embodies features frequently observed; and 'assimilative' models, which use data of different types and from as many locations as possible, but all taken at the same time, in order to derive a snapshot (or series of snapshots) of the entire pattern. Each type of model has its own difficulties. Statistical models, by their very nature, smooth out flow features (e.g. the convection reversal, and the locus of sunward flow deep in the polar cap) which are not found at precisely the same invariant latitudes and magnetic local times on different days. Pattern recognition models are better at reproducing small-scale features, but the large-scale pattern can be a matter of interpretation. Assimilative models (such as AMIE) hold out the best hope for creating instantaneous, global convection patterns; however, the analysis technique tends to be most irregular (and least reliable) in the regions which are not well covered by in situ data. It appears that, at least at times, a four cell model with sunward flow at the highest and lowest latitudes, and antisunward flow in between, is consistent with the observations. At other times, the observations may be consistent with a two-cell convection pattern, but which includes significant meanders within the polar cap.

Moses, Julie J.↗
NASA NTRS2000

The Sensitivity of Model Ozone to Advective and Photochemical Processes in the High Latitude Winter Lower Stratosphere

Three dimensional chemistry and transport models (CTMs) contain a set of coupled continuity equations which describe the evolution of constituents such as ozone and other minor species which affect ozone. Both advection and photochemical processes contribute to constituent evolution, and a CTM provides a means to evaluate these contributions separately. Such evaluation is particularly useful when both terms are important to the modeled tendency. An example is the ozone tendency in the high latitude winter lower stratosphere, where advection tends to increase ozone, and catalytic processes involving chlorine radicals tend to decrease ozone. The Goddard three dimensional chemistry and transport model uses meteorological fields from the Goddard Earth Observing System Data Assimilation System, thus the modeled ozone evolution may reproduce the observed evolution and provide a test of the model representation of photochemical processes if the transport is shown to be modeled appropriately. We have investigated the model advection further using diabatic trajectory calculations. For long lived constituents such as N2O, the model field for a particular time on a potential temperature surface is compared with a field produced by calculating 15 day back trajectories for a fixed latitude longitude grid, and mapping model N2O at the terminus of the back trajectories onto the initial grid. This provides a quantitative means to evaluate two aspects of the CTM transport: one, the model horizontal gradient between middle latitudes and the polar vortex is compared with the gradient produced using the non-diffusive trajectory calculation; two, the model vertical advection, which is produced by the divergence of the horizontal winds, is compared with the vertical transport expected from diabatic cooling.

Douglass, A.↗
NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗
NASA NTRS2001

Impacts of Interannual Climate Variability on Agricultural and Marine Ecosystems

The El Nino - Southern Oscillation (ENSO) is the dominant mode of global interannual climate variability, and seems to be the only mode for which current prediction methods are more skillful than climatology or persistence. The Zebiak and Cane intermediate coupled ocean-atmosphere model has been in use for ENSO prediction for more than a decade, with notable success. However, the sole dependence of its original initialization scheme and the improved initialization on wind fields derived from merchant ship observations proved to be a liability during 1997/1998 El Nino event: the deficiencies of wind observations prevented the oceanic component of the model from reaching the realistic state during the year prior to the event, and the forecast failed. Our work on the project was concentrated on the use of satellite data for improving various stages of ENSO prediction technology: model initialization, bias correction, and data assimilation. Close collaboration with other teams of the IDS project was maintained throughout.

Cane, M. A.↗
NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

Here we assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. The baseline experiment assimilates satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). These baseline experiments are then compared with experiments that additionally assimilate Aquarius (version 5.0 Lilly and Lagerloef, 2008) and SMAP (version 2.0 Meissner and Wentz, 2016) SSS. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation over 0-8 month forecast lead-times and removing the salty bias from SMAP data helps to extend useful forecasts out to 12 month lead-times.

SSS↗
NASA NTRS2018

Synergistic and Collaborative Development Strategies for FV3 Powered Next Generation Unified Global Modeling System

The GFDL (Geophysical Fluid Dynamics Laboratory, NOAA - National Oceanic and Atmospheric Administration) Finite-­Volume Cubed-Sphere Dynamical Core (FV3) is a scalable and flexible dynamical core capable of both hydrostatic and non-hydrostatic atmospheric simulations. FV3 has been chosen as the dynamical core for the Next Generation Global Prediction System project (NGGPS), designed to upgrade the current NCEP (National Centers for Environmental Prediction, NOAA) operational Global Forecast System (GFS) to run as a unified, fully-coupled system in NOAA's Environmental Modeling System infrastructure. FV3 dynamic core has a long history of serving as the main engine for global atmospheric models at various Government and Academic Research Laboratories including NOAA: GFDL Climate Modeling Suite (AM4 (Atmosphere Modeling 4), CM4 (Climate Model 4) ESM4 (Earth System Model 4), Hiram (HIgh Resolution Atmospheric Model)); NASA: GMAO (Global Modeling and Assimilation Office) Goddard Earth Observing System Model (GEOS); and NCAR (National Center for Atmospheric Research) Community Earth System Model (CESM). The three primary stakeholders in FV3 (GFDL, GMAO, EMC (Environmental Modeling Center - NOAA)) have embarked on synergistic and collaborative strategies with focus on the advancement of non-hydrostratic dynamic core, physics, chemistry, and data assimilation efforts, leveraging collective strengths of each of the partnering agencies. This talk presents the ongoing plans for model advancements at GFDL, GMAO and EMC, with special emphasis on contributions towards developing community based unified global modeling system for operational and research applications at respective organizations. Future plans include extending the collaborations to the development of earth system components including GMAO's aerosol chemistry models (GOCART/MAM (Goddard Chemistry Aerosol Radiation and Transport / Modal Aerosol Model)), Land Information System (LIS), and advanced data assimilation techniques; and GFDL's Modular Ocean Model (MOM) and Sea Ice Simulator (SIS) for transition to operations at NCEP.

Putman, William↗
NASA NTRS2008

Space-based Ornithology-Studying Bird Migration and Environmental Change in North America

Natural fluctuations in the availability of critical stopover sites coupled with anthropogenic destruction of wetlands, land-use change, and anticipated losses due to climate change present migratory birds with a formidable challenge. We have developed an individual-based, spatially explicit bird migration model that simulates the migration routes, timing and energy budgets of individual birds under dynamic weather and land surface conditions. Our model incorporates biophysical constraints, individual bird energy status, bird behavior, and flight aerodynamics. We model the speed, direction, and timing of individual birds moving through a user specified Lagrangian grid. The model incorporates environmental properties including wind speed and direction, topography, dynamic hydrologic properties of the landscape, and environmental suitability. The model is driven by important variables estimated from satellite observations of the land surface, by data assimilation products from weather and climate models, and biological field data. We illustrate the use of the model to study the impact of both short- and long-term environmental variatios, e.g. climate, drought, anthropogenic, on migration timing (phenology), spatial pattern, and fitness (survival and reproductive success). We present several theoretical simulations of the spring migration of Pectoral Sandpiper (Calidris melanotos) in North America with emphasis on the Central flyway from the Gulf of Mexico to Alaska.

Smith, James↗
NASA NTRS2005

Extratropical signature of the quasi-biennial oscillation

Using the assimilated data from the National Centers for Environmental Prediction (NCEP) reanalysis, we show that the extratropical signature of the tropical quasi-biennial oscillation (QBO) is seen mostly in the North Annular Mode (NAM) of atmospheric variability. To understand the extratropical manifestation of the QBO, we discuss two effects that have been suggested earlier: (1) The extratropical circulation is driven by the QBO modulation of the planetary wave flux, and (2) the extratropical circulation is driven by the QBO-induced meridional circulation. We found that the first effect is seen in wave 1 in the beginning of winter and in wave 2 in the end of winter. The QBO-induced circulation affects midlatitude regions over the entire winter. To investigate the QBONAM coupling, we use an equation that relates the stream function of the meridional circulation and the polar cap averaged temperature, which is a proxy for the NAM index. In addition to the annual (omega)a and the QBO frequency (omega)Q the spectrum of its solutions indicates the satellite frequencies at (omega)a +/- (o.

tropical quasi-biennial oscillation (QBO)↗
NASA NTRS2004

Simulation of Avifauna Distributions Using Remote Sensing

Remote sensing has proved a fruitful tool for understanding the distribution and functioning of plant communities at multiple scales and to understand their coupling to bioclimatic and anthropogenic factors. But a similar approach to understanding the distribution and abundance of bird species as well as many other animal organisms is lacking. The increasing need for such understanding is evident with the recent examples of threats to human health via avian vector transmission and the increasing emphasis on global conservation biology. From experimental observations we know that species richness tends to track biological or environmental gradients. In this paper, we explore the fundamental idea that thermal and water-relation environments of birds, as estimated from satellite data and biophysical models, can define the constraints on their Occurrences and richness. We develop individual bird energy budget models and use these models to define the climate space niche of birds. Using satellite data assimilation products to drive our models, we disperse a distribution of virtual or actual bird species across the landscape in accordance to the limits expressed by their climate space niche. Here, we focus on the North American summer breeding season and give two examples to illustrate our approach. The first is a tundra loving bird, e.g. corresponding to the Culidris genus, and a second genus example, Myiurchus, that corresponds to arid or semi-arid regions. We define these birds in terms of their basic physiology and morphological characteristics, construct avian energetic simulations to predict their allowable metabolic ranges and climate space limits.

Smith, James A.↗
NASA NTRS2011

Diagnosing the Sensitivity of Local Land-Atmosphere Coupling via the Soil Moisture-Boundary Layer Interaction

The inherent coupled nature of earth s energy and water cycles places significant importance on the proper representation and diagnosis of land atmosphere (LA) interactions in hydrometeorological prediction models. However, the precise nature of the soil moisture precipitation relationship at the local scale is largely determined by a series of nonlinear processes and feedbacks that are difficult to quantify. To quantify the strength of the local LA coupling (LoCo), this process chain must be considered both in full and as individual components through their relationships and sensitivities. To address this, recent modeling and diagnostic studies have been extended to 1) quantify the processes governing LoCo utilizing the thermodynamic properties of mixing diagrams, and 2) diagnose the sensitivity of coupled systems, including clouds and moist processes, to perturbations in soil moisture. This work employs NASA s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) mesoscale model and simulations performed over the U.S. Southern Great Plains. The behavior of different planetary boundary layers (PBL) and land surface scheme couplings in LIS WRF are examined in the context of the evolution of thermodynamic quantities that link the surface soil moisture condition to the PBL regime, clouds, and precipitation. Specifically, the tendency toward saturation in the PBL is quantified by the lifting condensation level (LCL) deficit and addressed as a function of time and space. The sensitivity of the LCL deficit to the soil moisture condition is indicative of the strength of LoCo, where both positive and negative feedbacks can be identified. Overall, this methodology can be applied to any model or observations and is a crucial step toward improved evaluation and quantification of LoCo within models, particularly given the advent of next-generation satellite measurements of PBL and land surface properties along with advances in data assimilation schemes.

Santanello, Joseph A., Jr.↗
NASA NTRSDate not supplied

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗
NASA NTRS2007

Clouds in GEOS-5

The GEOS-5 atmospheric model is being developed as a weather-and-climate capable model. It must perform well in assimilation mode as well as in weather and climate simulations and forecasts and in coupled chemistry-climate simulations. In developing GEOS-5, attention has focused on the representation of moist processes. The moist physics package uses a single phase prognostic condensate and a prognostic cloud fraction. Two separate cloud types are distinguished by their source: "anvil" cloud originates in detraining convection, and large-scale cloud originates in a PDF-based condensation calculation. Ice and liquid phases for each cloud type are considered. Once created, condensate and fraction from the anvil and statistical cloud types experience the same loss processes: evaporation of condensate and fraction, auto-conversion of liquid or mixed phase condensate, sedimentation of frozen condensate, and accretion of condensate by falling precipitation. The convective parameterization scheme is the Relaxed Arakawa-Schubert, or RAS, scheme. Satellite data are used to evaluate the performance of the moist physics packages and help in their tuning. In addition, analysis of and comparisons to cloud-resolving models such as the Goddard Cumulus Ensemble model are used to help improve the PDFs used in the moist physics. The presentation will show some of our evaluations including precipitation diagnostics.

Bacmeister, Julio↗
NASA NTRS1998

Surface Hydrology in Global River Basins in the Off-Line Land-Surface GEOS Assimilation (OLGA) System

Land surface hydrology for the Off-line Land-surface GEOS Analysis (OLGA) system and Goddard Earth Observing System (GEOS-1) Data Assimilation System (DAS) has been examined using a river routing model. The GEOS-1 DAS land-surface parameterization is very simple, using an energy balance prediction of surface temperature and prescribed soil water. OLGA uses near-surface atmospheric data from the GEOS-1 DAS to drive a more comprehensive parameterization of the land-surface physics. The two global systems are evaluated using a global river routing model. The river routing model uses climatologic surface runoff from each system to simulate the river discharge from global river basins, which can be compared to climatologic river discharge. Due to the soil hydrology, the OLGA system shows a general improvement in the simulation of river discharge compared to the GEOS-1 DAS. Snowmelt processes included in OLGA also have a positive effect on the annual cycle of river discharge and source runoff. Preliminary tests of a coupled land-atmosphere model indicate improvements to the hydrologic cycle compared to the uncoupled system. The river routing model has provided a useful tool in the evaluation of the GCM hydrologic cycle, and has helped quantify the influence of the more advanced land surface model.

Bosilovich, Michael G.↗
NASA NTRS2017

WCRP Task Team for the Intercomparison of Reanalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. Several centers have emerged to produce new atmospheric reanalyses in various forms every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses. Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While WCRPs involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses, a central team of reanalyses expertise is not in place in the WCRP structure. The differences among reanalyses and their inherent uncertainties are some of the most important questions for both users and developers of reanalyses. Therefore, a collaborative effort to systematically assess and intercompare reanalyses would be a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to the TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means 2)To communicate new developments and best practices among the reanalyses producing centers 3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment 4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific community. This presentation outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far.

WDA↗