Wednesday, April 26, 2017
   Search     
Enter Search Value:
- without any prefix or suffix to find all records where a column contains the value you enter, e.g. Net
- with | prefix to find all records where a column starts with the value you enter, e.g. |Network
- with | suffix to find all records where a column ends with the value you enter, e.g. Network|
- with | prefix and suffix to find all records containing the value you enter exactly, e.g. |Network|

Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Understanding changes in the regional variability of U.S. Drought

View abstract
North American drought is among the costliest extreme climatic events, with impacts to the U.S economy of several billion dollars per year. Droughts are typically amplified during the warm season and can rapidly intensify, as the flash drought of 2012 can attest. Some recent studies suggest that during meteorological summer (JJA) the interannual variability of North American precipitation has been increasing over the last 6 decades. However, upon closer examination it is evident that trends in this variability are more nuanced than previously thought. During spring (AMJ) both the Great Plains and Southeast show an increasing trend in interannual variability of precipitation over 1950-2010, however, during summer (JAS) the changes in variability exhibit a more decadal-like appearance. These regional precipitation variations during the spring and summer are acutely sensitive to North American low-level jet (NALLJ) variability. This dynamical feature of the atmosphere acts as a scale transfer mechanism between the large-scale forcing and regional climate variability.

The main goals of this proposal are to (1) Advance the understanding of the physical mechanisms linking changes in NALLJ fluctuations and regional precipitation variability, (2) Determine the ability of the current generation of global climate models to simulate and predict NALLJ variability and its related precipitation impacts, (3) Examine the roles of natural climate variability and anthropogenic climate change to recent changes in regional precipitation and NALLJ variability. These goals will be achieved by completing the following tasks: (task-1) expand the observational analysis of NALLJ variability and regional warm season precipitation variations to include the entire 20th century; (task-2) diagnose the role of global SST variability on the NALLJ modes and precipitation in observations and multi-model AMIP simulations; (task-3) examine the predictability of NALLJ variability modes in the National Multi-Model Ensemble (NMME) reforecasts; (task-4) Develop an experimental NALLJ prediction system; (task 5) evaluate the role of GHG increases on changes to regional precipitation variability using several thousand seasonal realizations provided by the NMME effort.

The proposed work contributes directly to a high priority topic for the NOAA FY 2014 MAPP funding Priority Area-1, Research to Advance Understanding, Monitoring, and Prediction of Drought: (i) “Understanding predictability of past droughts over North America”. This work will be conducted under the auspices of the NCEP Climate Prediction Center and will enable NOAA to achieve the major objectives of MAPP especially, “improving methodologies for global to regional-scale analysis, predictions, and projections” and “developing integrated assessment and prediction capabilities relevant to decision makers based on climate analyses, predictions, and projections.” The work is also highly relevant to NOAA’s goals as expressed in the NOAA Next generation Strategic Plan, specifically, Weather Ready Nation: Society is prepared for and responds to weather-related events, and Climate Adaptation and Mitigation: An informed society anticipating and responding to climate and its impacts.

Principal Investigator (s): Weaver, Scott (NOAA/CPC)

Co-PI (s): N/A
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Understanding the Role of Land-Atmospheric Coupling in Drought Forecast Skill for the 2011 and 2012 US Droughts

View abstract
The overall goals of the proposed project is to understand land-atmospheric coupling processes in CFSv2 and their role in the predictability of drought development, intensification and termination, and to perform attribution and modeling studies for the improvement of drought predictions.

Background. In 2011 and 2012, the central US suffered intense drought. While the societal impact of these extreme events can be reduced through planning and preparation, the predictive skill of seasonal forecasts from models such as NCEP’s CFSv2 is low, which limits their practical use. This is particularly true during the North American summer, when the need for predictions is the greatest.

Recent research by PI Wood’s group has resulted in a better understanding of the role that land- atmosphere interactions play in drought predictability in seasonal forecast models. This work developed the Coupling Drought Index (CDI) to assess the representation of land-atmosphere feedbacks in forecasts. The research has shown that the hindcast climatological CDI in CFSv2 quickly deviates from the reanalysis-based CDI into a wetter state and demonstrated that land-atmospheric coupling breaks down in CFSv2 during drought conditions (dry coupling) leading to the weakening and premature termination of the drought conditions. The loss of seasonal forecast drought skill is attributed to the failure of CFSv2 to hold drought conditions, especially in the major droughts of 2011 and 2012.

Preliminary analyses indicate that increased (anomalous) terrestrial evapotranspiration in CFSv2 is leading to its inability to hold drought conditions. One hypothesis is that deep soil water is accessed in the Noah land model and evaporated to control a warm bias while an alternative hypothesis is that the increase in evapotranspiration is due to a lack of dynamic vegetation in the model, which allows for continued transpiration during a drought event (due to the use of a vegetation phenology based on climatology)

Summary of Proposed Work: To address the goal of the proposed project, a combination of historical (realtime) and prescribed CFSv2 forecasts will be used to compare with verification data (CFSR/CDAS) to analyze the local feedback mechanism, the large scale circulation and their interactions in the development, intensification and termination of the 2011 and 2012 droughts in North America. Specifically,
1. An analysis of the CFSv2 forecasts (leads out to September) made from April through June for each event. The CFSv2 ensemble forecast data will be used to compute time series of the coupling states and CDI following the approach of Roundy et al., 2013a, b). The CFSv2 skill will be compared to a benchmark based on a CDI-based Statistical Drought Forecast Model.
2. Recycling analysis to track the moisture sources of CFSv2 anomalous precipitation, to assess if the moisture is from local sources (anomalous ET in CFSv2) or from large scale advection.
3. CFSv2 ensemble reforecast experiments for the droughts of 2011 and 2012 to examine the role of vegetation parameterization in CFSv2 (Noah). The experiments will use real-time vegetation fraction observations and an advanced Noah land model with Multiple Parameterization (Noah-MP) that includes both fixed and dynamic vegetation options.

Relevance to the Program. The research directly addresses the needs identified in the call: “Proposals (that) will examine processes controlling drought development, intensification, and termination with a focus on predictability. Specifically, (work that) consider mechanistic studies involving model simulations and predictions to examine processes such as the role of land surface conditions, … and atmospheric feedbacks..”. The significance of the research is that the work will help understand the processes that lead to premature termination of drought in CFSv2 forecasts and its low forecast skill. Based on the anticipated results, the project can identify potential CFS model improvements.

Principal Investigator (s): Wood, Eric (Princeton University)

Co-PI (s): Michael Ek (NOAA/EMC)
Year Initially Funded: 2014
Task Force:
Final Report:

Improved probabilistic forecasts for the NMME seasonal forecast system

View abstract
The North American Multi-Model Ensemble (NMME) forecasting project has been continuously producing seasonal forecasts since August 2011. Forecasts from the NMME, which brings together all the major U.S. climate forecasting institutions, as well as Environment Canada, have been extensively accessed by users in the broader community, and contribute to NOAA official climate forecasts. Currently, NMME forecasts are issued in a deterministic format, with experimental probabilistic forecasts. The proposed project will improve the NMME probabilistic forecasts through addressing systematic biases, allowing for more precise calibration of the forecast anomalies and probabilities. The final product of this project is expected to have greater reliability and accuracy, with the result of higher-quality NMME monthly and seasonal probability forecasts issued each month.

Improvements to the probabilistic forecasts will come from spatial calibrations and local probability anomaly calibrations. Spatial biases, i.e. errors in the positions of the patterns of the positive and negative anomalies being forecast, will be treated using a multivariate statistical method developed using the model’s hindcasts, prior to the local calibrations, making possible more skillful forecasts. Probability anomalies – the departure from climatological probabilities – may be too large for the skill that the forecasts possess. The probability anomalies will be tuned using a damping factor derived from the probability anomaly correlation. This calibration of the probability anomalies will be applied to both the individual model probabilities and to the overall NMME forecast probability.

The spatial and local calibrations will be applied individually and in combination, and the resulting probabilistic forecasts will be verified against observations for cross-validated skill using the Brier skill score and RPSS. While it is anticipated that the full set of calibrations will result in the most skillful forecast, this will be tested and quantified empirically.

The proposed project targets the Climate Test Bed, Research to Advance NOAA’s Operational Systems for Climate Prediction. The project will improve NOAA’s operational climate prediction by enhancing the utility and skill of the NMME forecasts. Without an accurate probability forecast, the ability of forecasters to assess confidence and uncertainty in the NMME is limited. This project will perform corrections to systematic biases, calibrate forecast anomalies, assess reliability and probabilistic skill, and transition the experimental probability forecast product into operations.

The proposed project supports the objectives of NOAA’s long-term climate goal by improving climate services. More accurate and reliable monthly and seasonal probabilistic climate forecasts are of benefit to the general public because they help alert businesses and individuals to the probabilities of anomalous climate for the immediately forthcoming seasons. Climate anomaly forecasts can result in the protection of life and property due to alertness and timely preparations, and probabilistic forecasts allow for users to react to forecasts within their own risk tolerance levels.

Principal Investigator (s): Barnston, Tony (Columbia University/IRI)

Co-PI (s): Huug van den Dool (NOAA/CPC), Emily Becker (NOAA/CPC), Michael Tippett (Columbia University/IRI), Shuhua Li (Columbia University/IRI)
Year Initially Funded: 2014
Task Force:
Final Report:

Improving the NCEP Climate Forecast System(CFS) through Enhancing the Representation of Soil-Hydrology-Vegetation Interactions

View abstract
The overarching goal of this collaborative effort is to improve the NCEP Climate Forecast System (CFS) forecast skill by enhancing the representation of soil-hydrology-vegetation interactions through the use of the new community Noah-MP (Multiple-Parameterization) land surface model (LSM). Numerous studies have illustrated the substantial influence of land-atmosphere interactions on seasonal-to interannual prediction. Soil moisture memory has been identified as a key in determining seasonal predictability in climate forecast systems. Improving soil-moisture related processes (e.g., evaporation, runoff, and groundwater) is important for potentially enhancing seasonal predictability of temperature and precipitation, which has direct benefit to the other MAPP call for “Research to Advance Understanding, Monitoring, and Prediction of Droughts”.

Compared to the earlier version CFS v1, CFS v2 slightly improved 2-m temperature prediction, but not precipitation anomaly. The Noah LSM used in the CFS v2 has been shown to produce low soil moisture with too high seasonal amplitude, which raises significant concerns about the residence times of soil moisture and reduces credibility of coupling with the atmosphere. The prediction skill of the next generation NCEP CFS v3can be enhanced through improving the Noah LSM with the Multiple-Parameterization (Noah-MP) modeling framework, which has been well tested and verified in seasonal regional climate simulations. Developed from prior NOAA CPPA support, the Noah-MP is a new-generation community LSM and treats key components of the land system with optional degrees of complexity. Since its first release in 2011 (Noah-MP v1), a number of model enhancements have been made to the Noah-MP v2 including added parameterization options for water-table physics, for canopy radiation and turbulence, modifications to snow schemes, and new soil-moisture stress functions in the photosynthesis model among others. Those enhancements are important to accurately represent land-atmosphere interactions and soil moisture memory in climate models.

This proposal will leverage on the ongoing work of the NCEP/EMC land team regarding the testing of Noah-MP v1 in CFS v2 and further evaluate and improve the newly released community Noah-MP v2, and address the overall scientific and operational questions: To what degree can a more accurate representation of soil-hydrology-vegetation interactions improve CFS seasonal predictions? We will collaborate with the NCEP, CFS, and Climate Test Bed teams to perform the following work: Task1: Benchmark performance of CFS v2 hindcast using different land models for a selection of nine years using the NCEP verification metrics; Task 2: Explore Noah-MP physics-ensemble forecasting by conducting numerous uncoupled GLDAS and coupled CFS hindcast experiments with different configurations of Noah-MP physics options; Task 3: Analyze ensemble spread and determine an optimal set of Noah-MP physics options that can maximize the CFS forecast evaluation metrics. We will ascertain whether an optimal set of Noah-MP physics exists and, if so, how it should be used in CFS; and Task 4: Understand the impact of soil-hydrology-vegetation seasonal prediction skill.

Principal Investigator (s): Chen, Fei (NCAR)

Co-PI (s): Zong-Liang Yang (University of Texas at Austin), Michael Ek (NOAA/EMC), Rongqian Yang (NOAA/EMC), Jesse Meng (NOAA/EMC)
Year Initially Funded: 2014
Task Force:
Final Report:

Subseasonal NMME Forecasts: Skill, Predictability, and Multi-model Combination

View abstract
We propose to systematically diagnose predictability and skill on subseasonal time scales (14-60 days) of all North American Multi-Model Ensemble (NMME) models, and to develop a statistically informed protocol for generating multi-model, subseasonal predictions. The statistical methodology for these goals has been developed in previous research but not yet applied systematically to subseasonal forecasts. We will diagnose predictability and skill of all NMME models using the same objective criteria for all relevant lead times, target days, and models. Since predictability is a necessary condition for skill, this diagnosis will help forecasters decide when and where NMME forecast can be utilized beyond 14 days. We will rigorously identify significant differences in predictability and skill, which will help forecasters understand when and where model forecasts may be misleading due to unrealistically large signals or unrealistically small noises. We will rigorously assess whether the skill of a combination of subseasonal forecasts is significantly greater than that of a single forecast. This diagnosis will provide guidance on multi-model forecast protocols, especially whether resources should be spread across multiple models or concentrated on a single good model. We will objectively decide the number of lags that can be included in a lagged ensemble sub-seasonal forecast. This diagnosis will provide guidance on whether initialization on 5-day intervals is frequent enough to provide useful lagged ensembles. We will determine whether multi-model forecasts with different initialization frequencies can be combined to enhance skill; e.g., determine whether a forecast initialized every 5 days can be usefully combined with a forecast initialized every day. We will test whether forecasts capture linear impacts of the MJO, and if not, provide correction algorithms. Such corrections will allow forecasters to recognize shortcomings of the forecast during certain phases of the MJO. We will apply LASSO to combine forecasts to produce a single forecast with superior skill. LASSO has the attractive property of setting weights identically to zero, and therefore provides a natural way to identify models in a multi-model ensemble that should be dropped for that target day and lead time. The resulting LASSO multi-model regression will give predictions with optimized skill, and information regarding the models assigned to zero weight will help forecasters understand the relative performance of models.

The proposed research responds directly to the MAPP-CTB program call “to improve operational systems for climate prediction” and to NOAA’s long term goal in Climate Adaptation and Mitigation “to produce accurate predictions” and “inform decision making” by establishing a rigorous basis for skillful subseasonal prediction.

Principal Investigator (s): DelSole, Tim (George Mason University)

Co-PI (s): Michael Tippett (Columbia University/IRI), Kathy Pegion (George Mason University)
Year Initially Funded: 2014
Task Force:
Final Report:

Advances in Lake-Effect Process Prediction within NOAA's Climate Forecast System for North America

View abstract
This proposal intends to incorporate a numerically efficient, physically based lake model into NCEP’s operational Climate Forecast System (CFS) version 2 in order to advance climate prediction at intraseasonal to interannual (ISI) time scales in North America. The proposed work is in response to the request for proposals by the Modeling, Analysis, Predictions, and Projections (MAPP) Program, which is partnering with the Climate Test Bed (CTB) to foster stronger operational practices for climate prediction at NCEP. The project will be completed under collaboration between Utah State University (USU) and the Land-Hydrology Team at NCEP’s Environmental Modeling Center (EMC). North America has the largest total lake volume and surface area of any continent on Earth. Lakes in this continent alter precipitation and temperature patterns at various spatial and temporal scales because they have smaller surface roughness, lower surface reflectance, and higher heat capacity than the nearby land; some of them also trigger severe storms during early winter and spring (e.g., the Great Lakes). However, in the current version of the operational CFS, running at approximately 100-km resolution, lake processes and their interactions with the atmosphere are largely neglected, potentially degrading climate forecasting skill.

For this project, we propose to couple an existing physically based lake model into the CFS to dynamically predict lake processes and their effects on climate in North America at ISI time scales. The lake model selected is the freshwater lake (FLake) model, which is a one-dimensional, two-layer energy and mass balance model. It includes the parameterizations of lake thermocline, lake ice and snow, and surface momentum, water, and heat fluxes. FLake has been implemented in several operational and research climate models across the world, resulting in improved predictions of lake-atmosphere interactions and thermal conditions for different-sized lakes at hourly to interannual time scales. During this project, retrospective forecasts with the coupled CFS-FLake model will be performed for historical periods and will be quantitatively evaluated using standard NCEP metrics for model evaluation with a focus on lake-related processes. The coupling work will provide a framework for the next CFS (version 3). The tasks proposed above fully comply with the operational activities for climate prediction at NCEP and are supported by the CTB, EMC, and Climate Prediction Center. The proposed studies are also consistent with the mission of MAPP, that is, to “enhance the Nation’s capability to predict variability and changes in the Earth’s climate system,” and will augment the current capacity of MAPP’s Climate Prediction Task Force to better understand and predict climate variability at ISI time scales.

Principal Investigator (s): Jin, Jiming (Utah State University)

Co-PI (s): Michael Ek (NOAA/EMC), Yihua Wu (NOAA/EMC)
Year Initially Funded: 2014
Task Force:
Final Report:

Improving cloud microphysics and their interactions with aerosols in the NCEP Global Models

View abstract
We propose a two-year research-to-operation project to the NOAA MAPP Program to enhance the NOAA/NCEP weather-climate modeling capabilities by improving the representations of cloud microphysics, aerosol processes, and aerosol-cloud-radiation interactions in the NCEP global models (i.e., the Global Forecast System, GFS, and the Climate Forecast System, CFS). Our proposed work responds directly to Competition 2 of the MAPP 2014: Climate Test Bed - Research to Advance NOAA’s Operational Systems for Climate Prediction.

While understanding the climate impacts of the complex cloud-aerosol-radiation interactions remains a major frontier in climate sciences, there have been significant processes in developing process-level representations of clouds and aerosols as well as in understanding the processes relevant to aerosol-cloud-radiation interactions. NASA GMAO is revamping the existing treatments of clouds and aerosols in Goddard Earth Observing System Model, Version 5 (GEOS-5) by introducing a double-moment cloud microphysics scheme (Morrison and Gettleman, 2008) and coupling it with a modal aerosol model (Liu et al., 2012). Both schemes are developed and implemented in the Community Atmosphere Model (CAM5.1), the atmospheric component of the Community Earth System Model (CESM) primarily at the National Center for Atmospheric Research (NCAR). This project will adopt the physically-based cloud/aerosol package at GMAO, which in turn leverage scientific advances by a broad climate research community.

At NCEP, major development work is underway to advance the representation of atmospheric physical processes in the GFS and CFS. This proposal is closely aligned with, and complementary to, these ongoing GFS/CFS research and development activities. Furthermore, this SUNYA-NCEP-GMAO collaborative project builds on and further strengths the existing NCEP-GMAO partnership. The outcomes of this project support NOAA's long-term goals and objectives as highlighted in its Next Generation Strategic Plan (NGSP). Specifically, this project will contribute toward achieving the first of the NGSP climate objectives, an improved scientific understanding of the changing climate system and its impacts, by improving two core capabilities: understanding and modeling, and predictions and projections.

Principal Investigator (s): Lu, Sarah (University of Albany, SUNY)

Co-PI (s): Yu-Tai Hou (NOAA/EMC), Arlindo da Silva (NASA/GSFC), Shrinivas Moorthi (NOAA/NCEP), Fanglin Yang (NOAA/NCEP), Qilong Min (University of Albany, SUNY), Anton Darmenov (NASA/GSFC), Donifan Barahona (NASA/GSFC)
Year Initially Funded: 2014
Task Force:
Final Report:

Assessment of CFS predictions of U.S. severe weather activity

View abstract
Each year tornadoes affect the U.S., with some years and periods being considerably more active than others. Attributing tornado activity to any particular large-scale climate phenomena is understandably difficult given the tremendous disparity between the time and space scales of tornadoes and those of large-scale climate phenomena. However, emerging science suggests that low-frequency (time-scales of a week to months) modes of climate variability may modulate severe weather activity and severe weather environments.

The likelihood of severe convective weather is related to the local atmospheric environment. Information about the environmental “ingredients” associated with severe weather has proved useful to forecasters in interpreting observed soundings and short-range forecasts. These empirical relations, summarized in the form of indices, have recently been shown to capture aspects of the climatology and year-to-year variability of U.S. severe weather on continental and regional scales. Moreover, predicted monthly index values based on the operational NOAA Climate Forecast System version 2 (CFSv2) have been demonstrated to have statistically significant skill.

Given these developments, it is reasonable that operational forecast models may be able to capture meaningful modulation of severe weather environments, and thereby provide forecasters with extended-range guidance about severe weather activity. Two primary obstacles to forecaster use of such tools are the lack of suitable skill assessments and the lack of methodologies with which to identify low-uncertainty forecasts (e.g., on the basis of forecast consistency). The purpose of this project is to provide such assessments and tools for CFSv2 forecasts as well as for medium-range forecast models like the one used in the ESRL/PSD second-generation Reforecast Project.

The main proposed activities are:
- Assessment of systematic model biases as a function of location, start time and lead-time.
- Assessment of skill dependence on lead-time and target period averaging window.
- Development of tools to identify and visualize forecast consistency (across ensemble members and from different forecast runs).
- Case studies examining the relation between forecast skill and consistency for notable events.

This work is highly relavant to NOAA’s long-term goal of climate adaptation and mitigation described in NOAA’s Next-Generation Strategic Plan (NGSP)–in particular, objective (1) Improved scientific understanding of the changing climate system and its impacts and connections between weather and climate, for instance, how climate affects severe weather events.

This proposal targets the Modeling, Analysis, Predictions, and Projections (MAPP) Competition: Climate Test Bed—Research to Advance NOAA’s Operational Systems for Climate Prediction and focuses on the activity 2: “the performance of experimental prediction methodologies.” This proposal will directly benefit NOAA products such as the U.S. Hazards Outlook. The U.S. Hazards Outlook is produced daily at NOAA’s Climate Prediction Center (CPC) and highlights potential U.S. hazards including extreme temperatures, heavy precipitation, flooding, wildfires, high winds and waves as well as severe weather. The CPC coordinates closely with the Storm Prediction Center (SPC) in identifying hazard areas during the upcoming forecast time period. SPC forecasters currently use a variety of guidance to compile the daily updates to the fire weather and severe weather components. However, little research exists to support forecasts for hazards beyond day 5. The work in this proposal will address the lack of suitable verification metrics and forecast consistency visualization tools and will enhance forecast operations at SPC and CPC.

Principal Investigator (s): Tippett, Michael (Columbia University/IRI)

Co-PI (s): Gregory Carbin (NOAA/NWS/SPC), Jon Gottschalck (NOAA/CPC)
Year Initially Funded: 2014
Task Force:
Final Report:

Bridging the gap in NOAA's extended and long range prediction systems through the development of new forecast products for weeks 3 and 4

View abstract
We propose to develop new operational temperature and precipitation forecast products over North America for lead times of 3 and 4 weeks that would bridge the gap between NCEP/CPC’s 8-14 day and monthly outlooks and complete a seamless prediction system that links NOAA’s intraseasonal and seasonal forecast products. At the foundation of this proposal, recent work by the PIs demonstrates the feasibility of a simple statistical forecast model that combines information from the Madden-Julian Oscillation (MJO), El Niño-Southern Oscillation (ENSO), and linear temperature trend to generate skillful North American wintertime temperature forecasts in weeks 3 and 4. To build upon this effort, the purpose of this project is (1) to transition this statistical model into an operational week 3 and week 4 temperature and precipitation outlook for all seasons; (2) to determine the feasibility of providing information on extremes at these lead times; (3) to calibrate CFSv2 forecasts with the use of archived reforecasts to evaluate the performance of products for weeks 3 and 4 based on the CFSv2; and (4) to explore extending the model calibration approach to the US National Multi-Model Ensemble (NMME).

Principal Investigator (s): Xie, Shang-Ping (University of California, San Diego)

Co-PI (s): Nat Johnson (University of Hawaii), Steven Feldstein (Penn State University), Michelle L'Heureux (NOAA/CPC), Stephen Baxter (NOAA/CPC)
Year Initially Funded: 2013
Task Force:
Climate Reanalysis Task Force
Final Report:

Research towards the next generation of NOAA Climate Reanalyses

View abstract
The fidelity of new reanalysis datasets (MERRA, 20CR, CFSR, ERA-Interim) at representing climate variability of the 20th century has enabled significant advances in climate research. In this research proposal, we will investigate known shortcomings of these datasets, while developing a framework for a new NOAA Climate Reanalysis (NCR) system to ameliorate them. The NCR will eventually have four “streams” to meet the various user needs for reanalysis information:

Stream 0: Boundary-forced, 1850-present “AMIP” simulation with large ensemble
Stream 1: Historical, 1850-present using only surface data
Stream 2: Modern, 1946-present using only surface and conventional upper air data
Stream 3: Satellite, 1973-present using quality-controlled satellites, Global Positioning System Radio Occultation, and surface and conventional upper air data.

One of the foci of this research will be to use observing system experiments. In these, the 2000-2010 observing system is reduced to that of selected historical periods to investigate the impact to the time-varying quality and density of the observing system and determine ways to reduce this impact. We will use innovative methods to assess the relative importance and impact of model errors and observational errors on the quality and homogeneity of the reanalysis fields, with particular attention to reducing or eliminating spurious jumps and trends. The framework for the NCR system will leverage recent advances in operational data assimilation for global weather prediction, as well as newly digitized observational datasets and global model improvements. While initially focusing on the atmosphere to develop the NCR framework, this project will serve as the basis for further NCR efforts, incorporating advancements generated by other projects supported by MAPP, such as integration of ocean, chemistry, and land components and the treatment of observational and model biases. International coordination and data sharing with NOAA's reanalysis partners at NASA, ACRE, ECWMF, and JMA and synergies from the NOAA Reanalysis Task Force will be crucial in achieving the project's goals on a limited budget.

This project is directly related to foci 1 of Priority 1 of the MAPP call for proposals. It is directly relevant to NOAA's Next-Generation Strategic Plan goals for climate adaptation and mitigation. As noted by the WMO Global Framework on Climate Services, reanalyses are a key component of the climate information needed for informed decision making for climate change mitigation and adaption. The NGSP recognizes that a strong scientific basis is needed for developing “climate adaptation and mitigation” strategies, which will require “improved scientific understanding of the changing climate system and its impacts” and “assessments of current and future states of the climate system.” For NOAA to achieve these objectives, we must develop climate reanalysis products that are free of artificial trends and that provide reliable information about the frequency of weather and climate extremes. This proposal directly addresses the MAPP call to pursue research on “Outstanding issues in atmospheric reanalysis”, in particular by attempting to “overcome the impact of data inhomogeneities due to changes in the observing system and data biases”, “overcome the impact of model bias”, “better quantify uncertainties in reanalysis data including the impacts of data and model error”, and “exploit new data”.

Principal Investigator (s): Kumar, Arun (NOAA/NCEP)

Co-PI (s): Gilbert Compo (CIRES/NOAA ESRL Lead Investigator), Jeffrey Whittaker (NOAA/ESRL), Prashant Sardeshmukh (NOAA/ESRL), Russell Vose (NOAA/NCDC)
Page 7  of  14 First Previous 3 4 5 6 [7] 8 9 10 11 12 Next Last

The Modeling, Analysis, Predictions, and Projections (MAPP) Program's mission is to enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system. The MAPP Program supports development of advanced climate modeling technologies to improve simulation of climate variability, prediction of future climate variations from weeks to decades, and projection of long-term future climate conditions. To achieve its mission, the MAPP Program supports research focused on the coupling, integration, and application of Earth system models and analyses across NOAA, among partner agencies, and with the external research community.

Learn more...

Download our program brochure (pdf).