- Enhancement of high resolution hydrological modeling on the CONUS HRAP grid using operational NOAA NCEP and NOAA OHD models
Investigators: Brian Cosgrove (NOAA OHD), Jiarui Dong (NOAA EMC), Michael Ek (NOAA EMC), Kingtse Mo (NOAA CPC)
Contact Information: Brian Cosgrove - Brian.Cosgrove@noaa.gov, Jiarui Dong - Jiarui.Dong@noaa.gov
Project Description: This MAPP-funded project centers on supporting NOAA/EMC's and NOAA/OHD's operational hydrological and land surface modeling missions, as well as furthering their support of the National Integrated Drought Information System (NIDIS), NOAA Hydrology Test Bed, and the NOAA Climate Test Bed. New capabilities resulting from this joint NOAA EMC/OHD/CPC effort will allow for the execution of enhanced Noah and Sacramento Heat Transfer (SAC-HTET) models on the 4km HRAP grid over the Continental United States (CONUS) over a 33 year period using NLDAS2 forcing data. Enhancements will impact all stages of modeling operations and will include improved downscaled forcing data, spin-up strategies, data assimilator modules, model physics, and model validation procedures. SAC-HTET will be incorporated into LIS as will OHD's kinematic wave routing scheme, with the latter enabling national runoff routing of both Noah and SAC-HTET output. Additionally, for the National Integrated Drought Information System and CPC's monthly drought briefing activities, this research will yield 33-year high-resolution model climatologies of soil moisture, snow pack, evapotranspiration, stream flow and other hydrologically relevant variables in support of the monitoring and prediction of drought.
Temporally and Spatially Dynamic Downscaling of Temperature Forcing Data
Observed lapse rate (left), predicted lapse rate from statistical regression equation based on in-situ air temperature data (middle) and the difference between the predicted and the actual lapse rates (right) for June averaged over 2000-2005 time period. The statistical regression equation will be used in this project to produce a time- and space-varying lapse rate, which will then be used to downscale the NLDAS2 forcing data.
- NCEP Global Land Data Assimilation System (GLDAS)
Investigators: Michael Ek (NOAA EMC), Jesse Meng (NOAA EMC), Kingtse Mo (NOAA CPC)
Contact Information: Jesse Meng - Jesse.Meng@noaa.gov
Project Description: The NCEP GLDAS is a semi-coupled land surface modeling and assimilation system implemented within the NCEP Climate Data Assimilation System version 2 (CDASv2). The NASA Land Information System (LIS) infrastructure is used to execute the Noah land surface model, forced with CDASv2 atmospheric variables and observed precipitation, to generate enhanced global land surface hydrometerological fields such as soil moisture, soil temperature, snowpack, evapotranspiration, and runoff. Two global precipitation products of the NCEP Climate Prediction Center (CPC) are used: 1) the global daily gauge analysis and 2) the global pentad merged gauge-and-satellite CMAP analysis. Thirty-two years (1979-2010) global (38 km resolution) land surface dataset with continuous real-time extension is produced by GLDAS to provide the initial conditions for the hydroclimate forecasts on the seasonal time scales. GLDAS also provides a climatology reference to assess the historical, current, and predicted hydroclimate anomalies to support the proposing Global Drought Information System.
- Dual Assimilation of Microwave and Thermal-Infrared Satellite Observations of Soil Moisture into NLDAS for Improved Drought Monitoring
Investigators: Christopher Hain (University of Maryland), Martha Anderson (USDA ARS), Xiwu Zhan (NOAA NESDIS), Mark Svoboda (NDMC), Brian Wardlow (NDMC), Michael Ek (NOAA NCEP) and Wade Crow (USDA ARS)
Contact Information: Chris Hain - email@example.com, Martha Anderson - firstname.lastname@example.org
Project Description: This MAPP-funded project is developing an operational system for optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time vegetation fraction (GVF) into the NLDAS Noah land-surface model (LSM). It has been demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can reduce SM drifts in prognostic LSMs such as Noah. The two retrievals are quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25-60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root-zone, reflected in the observed canopy temperature. The DA system will support NOAA's National Drought Monitoring projects and will be used by authors of the US Drought Monitor in weekly drought assessments. Outputs will include weekly maps of surface and root-zone SM, ET and runoff and will be distributed through the NIDIS Drought Portal.
Seasonal standardized anomalies in US Drought Monitor drought classifications (top left) and in SM estimates for April-October 2007 for Noah (LSM; top right), AMSR-E (MW; bottom left) and ALEXI ESI (TIR; bottom right). Red indicates drier than normal, green indicates wetter than normal conditions.
- Enhancing Seasonal Drought Prediction Capabilities for the US and the Globe Using the National Multi-Model Ensemble
Investigators: Bradfield Lyon (IRI), Kingtse Mo (NOAA CPC), Anthony Barnston (IRI)
Contact Information: Brad Lyon - email@example.com, Kingtse Mo - Kingtse.Mo@noaa.gov
This project, funded by MAPP will build on probabilistic seasonal drought prediction capabilities recently developed by members of the research team by incorporating NMME forecasts into that framework. The overall goal of the project is to enhance current seasonal drought prediction efforts over the US while also developing a prototype drought prediction system for the globe. The work will blend observed drought conditions with the dynamical model precipitation (and temperature) forecasts to predict multiple "drought" indicators. The focus will be on generating objectively derived probabilities of future drought conditions (i.e., drought indicator values) given the current drought state at lead times of 1 to 8 months. Web-based tools will be developed for the interactive display of drought forecast information as well as historical drought conditions. The overall strategy is envisioned to be an intermediate complexity approach that can easily transition to near real time operations to support both NIDIS and GDIS drought information systems.
One-season lead forecast indicating the probability that the 9 month standardized precipitation index (SPI9) will be less than -1.0 at the end of August 2011 given its initial condition in May 2011 combined with the IRI multi-model seasonal precipitation forecast for June-July-August. Similar tools will be developed using the NMME and for the globe as well as for the US.
- Exploring best practice procedures for optimal use of climate forecast for regional hydrological applications
Investigators: Lifeng Luo and Pang-Ning Tan (Michigan State University)
Contact Information: Lifeng Luo - firstname.lastname@example.org
Skillful seasonal climate prediction can significantly benefit the decision making in water resource management, agriculture and many other sectors. Currently, research is needed to assess the best practice prediction skills of the state-of-the-art climate forecast systems and to develop procedures for optimal use of climate forecasts in regional hydrological applications. This project will address these needs by carrying out research activities to 1) objectively evaluate prediction skill of CFSv2 and ENSEMBLES models and identify the source of predictability; 2) assess the optimal choice of ensemble members and scales and develop procedures for combining forecast information to achieve better forecast quality; and 3) demonstrate the usefulness of these procedures in seasonal drought predictions. The innovation of this research is reflected in the second activity in which we will develop two innovative methods (multiscale Bayesian merging and structured output regression) in parallel to combine forecast information across multiple characteristic spatial and temporal scales. The proposed methods are new to seasonal prediction, but have been used in research fields of data assimilation, data mining and machine learning. This research has the potential to significantly advance the seasonal climate forecast skills, thus directly contributing to NOAA's operation in seasonal climate forecasting.
Illustration of the multiscale abstraction of a spatial field (left) and the structured output regression method in machine learning (right). Both methods will be developed in parallel to combine forecast information across multiple scales.
Project Website: http://drought.geo.msu.edu/research/forecast/
- Atmosphere-Land Coupling and the Predictability of North American Drought
Investigators: Ben Kirtman (University of Miami - RSMAS)
Contact Information: Ben Kirtman - email@example.com
The MAPP funded research project is based on the hypothesis that the predictability of persistent large-scale drought is due the competition of among three processes:
(i) The nature of local coupled atmosphere-land feedbacks (i.e., strength, growth rate, saturation)
(ii) The predictability limiting affects of atmospheric noise or stochastic forcing
(iii) The remote forcing from low frequency global SST variability (e.g., AMO, PDO, NPO).
We propose to test this hypothesis through a series of modeling experiments that isolate the relative importance of coupled atmosphere-land feedbacks vs. atmospheric stochastic forcing vs. remote SST forcing. These experiments include using the novel interactive ensemble coupling strategy (Kirtman and Shukla 2002) previously used to isolate coupled ocean-atmosphere feedbacks vs. atmospheric stochastic forcing applied to the problem atmosphere-land interactions. Part of our modeling strategy builds on the success of the US Clivar drought WG (http://www.usclivar.org/Organization/drought-wg.html) and the international Global Land-Atmosphere Coupling Experiment (GLACE) by explicitly leveraging their experimental protocol. We have chosen to focus on the question of North American drought because of its societal importance to US interests; however, the approach is equally applicable to terrestrial hydro-climate predictability on multiple space and time scales throughout the globe. Current specific activities examine: (a) the impact of atmosphere-land initialization in seasonal prediction experiments, (b) the predictability of southeast US drought from a multi-model prediction perspective and (c) using the interactive ensemble coupling strategy to diagnose atmosphere-land feedbacks. The figure below indicates that land-atmosphere coupling has a large impact on internal atmospheric variability.
Estimate of internal atmospheric surface wind variability with atmosphere-land coupling (left panel) and without atmosphere-land coupling (right panel). The interactive ensemble approach is used to isolate the atmosphere-land coupling.
- Integrating Data Assimilation and Multi-modeling Within CHPS for Improved Seasonal Drought Prediction
Investigators: Hamid Moradkhani (Portland State University), Andrew Wood (NOAA NWRFC), Pedro Restrepo (NOAA OHD)
Contact Information: Hamid Moradkhani - firstname.lastname@example.org - http://web.cecs.pdx.edu/~hamidm/
This project proposes to develop methods and tools to quantify and reduce the major uncertainties involved in drought forecasting by implementing data assimilation and Bayesian multi-model combination in the context of seasonal hydrologic forecast system. We focus in particular on those observed variables that have been shown to have the greatest potential for improving hydrological forecasts in the western U.S., specifically in situ observations of Snow Water Equivalent; remotely sensed observations of Snow Cover Extent and streamflow. We will also investigate, on a more local basis, the potential for assimilation of in situ soil moisture given the difficulty and inaccuracy in using remotely sensed soil moisture fields for the western US. The system we propose will use Community Hydrologic Prediction System (CHPS) as a framework to incorporate few hydrologic models while infusing the methods of improving hydrologic initial conditions (by means of data assimilation) and its integration with Ensemble Streamflow Prediction (ESP).
Diagram of the ESP and ESP-DA algorithms. Also showing the individual traces generated from sampling the initial condition.
Cumulative daily volumetric flow plots over the upper Colorado River Basin. This figure shows the 95% predictive bounds of the cumulative runoff volume from ESP (black lines) and ESP-DA (green lines). The expected value of each is a dashed line and the cumulative observed runoff volume is the red line.
- Assimilating Soil Moisture and Snow Products for Improved Drought Monitoring with the North American Land Data Assimilation System (NLDAS)
Investigators: Christa Peters-Lidard (NASA), David Mocko (SAIC at NASA), Sujay Kumar (SAIC at NASA), Michael Ek (NOAA EMC), Youlong Xia (IMSG at NOAA EMC), Jiarui Dong (IMSG at NOAA EMC)
Contact Information: Christa Peters-Lidard - email@example.com, David Mocko - David.Mocko@nasa.gov
The primary tasks of this project are: 1) Bring the suite of NLDAS land-surface models (LSMs) under the Land Information System (LIS) architecture, including upgrades to the latest versions of the LSMs (Catchment, Noah, SAC-HTET/SNOW-17, and VIC); 2) Assimilate remotely-sensed soil moisture and snow data products using LIS; and 3) Evaluate the improvements from data assimilation to the depiction of the land-surface and drought states. Comparisons will be made to in situ and gridded observational and reanalysis datasets using the Land-surface Verification Toolkit (LVT). Historical droughts will also be examined using LVT and drought anomalies, percentiles, and indices, with a focus on three drought case studies identified by the NOAA MAPP Drought Task Force. An example from the 2007-2008 Southeastern U.S. drought is shown in Figure 1. Data from the next phase of NLDAS will be made available to the public via the NASA GES DISC. The data will span from January 1979 to present, and be available hourly at 1/8th-degree (about 12km) over CONUS and parts of Canada and Mexico, from 25 to 53 North. NLDAS data products will be made available in several methods for use in a wide array of applications.
The NASA NLDAS webpage (including documentation and a FAQ): http://ldas.gsfc.nasa.gov/nldas/The NASA GES DISC webpage with NLDAS datasets: http://disc.sci.gsfc.nasa.gov/hydrology/The Land Information System (LIS) webpage: http://lis.gsfc.nasa.gov/The NCEP/EMC NLDAS webpage: http://www.emc.ncep.noaa.gov/mmb/nldas/The NLDAS Drought Monitor: http://www.emc.ncep.noaa.gov/mmb/nldas/drought/
U.S. Drought Monitor (top) near the end of the 2007-2008 Southeast U.S. Drought, indicating drought severity category, and NLDAS Mosaic (bottom left) and NLDAS Noah (bottom right) soil moisture percentile. The colors and percentile intervals in the NLDAS drought monitor images were chosen to match those from the U.S. Drought Monitor.
- The North American Land Data Assimilation System (NLDAS)
Investigators: Michael Ek (NOAA EMC), Youlong Xia (NOAA EMC)
Contact Information: Dr. Michael Ek - Michael.Ek@noaa.gov, Dr. Youlong Xia - Youlong.Xia@noaa.gov
Currently NLDAS is a quasi-operational system at NCEP to support U.S. operational drought monitoring and seasonal hydraulic prediction (e.g., National Integrated Drought Information System). It consists of a retrospective 29-year (79-08) historical execution and a near real-time daily update execution using four land surface models on a common 1/8th degree grid using common hourly land surface forcing. The non-precipitation surface forcing is derived from the NCEP North American Regional Reanalysis (NARR). The precipitation forcing is anchored to daily gauge-only precipitation over Continental United Sates that applies Parameter-elevation Regressions on Independent Slopes Model corrections. The NARR-based surface downward solar radiation is bias-corrected using seven years (97-04) of satellite-derived solar radiation retrievals. Near real-time land states and water fluxes of each of the four models from daily executions are depicted as anomalies and percentiles with respect to their own model climatology, shown at the "NLDAS Drought" tab of the NLDAS website. NLDAS products are directly provided to the U.S. Drought Monitor author group daily. NLDAS will be implemented in NCEP operations in the near future. The NCEP/EMC NLDAS team is collaborating with the NASA to add its Land Information System to the current NLDAS system to allow assimilation of remotely-sensed data and in-situ observations.
Total column soil moisture percentiles in June 1988 for four land surface models and their ensemble mean (D0 - D4 is drought severity classification defined by U.S. Drought Monitor author group). NLDAS is a multi-institutional collaboration project including National Centers for Environmental Prediction (NCEP) and Office of Hydrologic Development (OHD) of National Oceanic and Atmospheric Administration (NOAA), Goddard Space Flight Center (GSFC) of National Aeronautics and Space Administration (NASA), Princeton University (PU), University of Washington (UW), and the other government agencies and universities (see http://www.emc.ncep.noaa.gov/mmb/nldas/LDAS8th/LDASparticipants.shtml). Four land surface models are Noah developed by NCEP, Mosaic developed by GSFC, SAC developed by OHD, and VIC jointly developed by PU and UW. More NLDAS drought monitoring information can be found in NLDAS website shown in Figure 1.
Research Website: NOAA/NCEP: http://www.emc.ncep.noaa.gov/mmb/nldas, NASA/GSFC: http://ldas.gsfc.nasa.gov/nldas
- A framework for improving land-surface hydrologic process representation in CLM over California
Investigators: Soroosh Sorooshian (University of California, Irvine)
Contact Information: Soroosh Sorooshian - firstname.lastname@example.org
Our study focuses on the modeling of hydroclimate system of the State of California. The state has experienced two major droughts and three major floods since the 1980's, and it continues to grow in terms of its population and expansion of urban and built environments. California's water supply systems are straining to keep up with the economic growth and meeting its expanding urban areas. The groundwater resource--which accounts for 30-40% of the water California uses -- is diminishing at a rate of millions of acre-feet per year. Combined, these regional changes pose an urgent need for accurate models and reliable predictions of key hydrologic processes impacted by regional climate change.
To understand the complex interaction between the natural hydroclimate system and the engineered-water resources system we are integrating a hydrological modeling system which includes: 1) a remote sensing satellite-based snow model, 2) a modified Community Land Model (CLM), and 3) a two-dimensional groundwater model-MODFLOW. Specific to this research, we are trying to improve estimations of interactive mechanisms using in-situ and satellite data: the partition of snow water into runoff, soil moisture and groundwater recharge; parameterization of seasonal irrigation and groundwater pumping over the state, and groundwater discharge/recharge to/from river flows.
We are also taking advantage of California's historical and present time records for model parameter calibration and using sequential data assimilation techniques to substantially improve model performance. Calibrating model parameters using information from long-term observations provides optimized values for key model parameters and is expected to reduce model uncertainty. The sequential data assimilation technique--the Sequential Bayesian Filter with Monte Carlo implementation (SBF-MC) has integrated near real-time hydrological measurements from satellites (e.g. top layer soil moisture) and/or ground sites (e.g. stream gauge data and groundwater well monitoring) to both improve the predictions of model state variables and quantify prediction uncertainty.
- A US National Multi-Model Ensemble ISI Prediction Experiment
Investigators: Benjamin Kirtman (University of Miami), Jim Kinter (Center for Ocean-Land-Atmosphere Studies), Dan Paolino, Michael K. Tippett, Anthony G. Barnston (The Earth Institute, Columbia University), Tony Rosati (NOAA GFDL), Kathy Pegion (CIRES University of Colorado), Siegfried Schubert, Michele Reinecker, Max Suarez (NASA GMAO), Huug Vandendool, Jin Huang, Malaquias Pena Mendez, Scott Weaver, Qin Zhang, Jon Gottschalck (NOAA CPC), Joe Tribbia, Don Middleton (NCAR), Eric F Wood (NOAA CPC)
The project leverages an existing National Multi-Model Ensemble (NMME) team that has already formed and is already producing routine real-time intra-seasonal, seasonal and interannual (ISI) predictions and providing them to the NOAA Climate Prediction Center (CPC) on an experimental basis for evaluation and consolidation as a multi-model ensemble ISI prediction system. The funded MAPP project (phase 2 NMME, or NMME-2). will develop a more "purposeful MME" in which the requirements for operational ISI prediction are used to define the parameters of a rigorous reforecast experiment and evaluation regime. The NMME team will design and test an operational NMME protocol (i.e., a purposeful MME) that is to guide the future research, development and implementation of the NMME beyond what can be achieved based on an "MME of opportunity."
The activity proposed here is to develop a more "purposeful NMME" in which the requirements for operational ISI prediction are used to define the parameters of a rigorous reforecast experiment and evaluation regime. A summary of the project activities are:
i. Build on existing state-of-the-art US climate prediction models and data assimilation systems used in NMME-1.
The proposed activity includes several NMME research themes:
ii. Fully consider operational forecast requirements (forecast frequency, lead time, duration, number of ensemble members, etc.) and regional/user specific needs, which includes considering the hydrology of various regions in the US and elsewhere in order to address drought and extreme event prediction.
iii. Utilize the NMME system experimentally in a near-operational mode to demonstrate the feasibility and advantages of running a NMME system as part of NOAA's operations.
iv. Enable rapid sharing of quality-controlled reforecast data among the NMME team members, and develop procedures for timely and open access to the data by the broader climate research and applications community.
i. The evaluation and optimization of the NMME system in hindcast mode (e.g., assessing the optimal number of ensemble members from each model, how to best combine the multi-model forecasts, sources of complementary prediction skill, etc.), methodologies to recalibrate individual dynamical models prior to combination, and provision of probabilistic quantitative (rather than categorical) information, and assessment of forecast skill at multiple time scales.
ii. Ocean and land initial condition sensitivity experiments.
iii. The application of the NMME forecasts for regional downscaling and hydrological prediction, including the forecasting of drought.
Percentages of skillful forecasts (Continuous Ranked Probability Skill Score: CRPSS>0) for seasonal mean precipitation and surface air temperature anomalies over all global land areas. N1-N6 are individual models from NMME, and E1-E5 are from ENSEMBLES. (Yuan and Wood, submitted)