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At least 55 records · Page 3
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NASA NTRS2010

Assessment of the Breakup of the Antarctic Polar Vortex in Two New Chemistry-Climate Models

Successful simulation of the breakup of the Antarctic polar vortex depends on the representation of tropospheric stationary waves at Southern Hemisphere middle latitudes. This paper assesses the vortex breakup in two new chemistry-climate models (CCMs). The stratospheric version of the UK Chemistry and Aerosols model is able to reproduce the observed timing of the vortex breakup. Version 2 of the Goddard Earth Observing System (GEOS V2) model is typical of CCMs in that the Antarctic polar vortex breaks up too late; at 10 hPa, the mean transition to easterlies at 60 S is delayed by 12-13 days as compared with the ERA-40 and National Centers for Environmental Prediction reanalyses. The two models' skill in simulating planetary wave driving during the October-November period accounts for differences in their simulation of the vortex breakup, with GEOS V2 unable to simulate the magnitude and tilt of geopotential height anomalies in the troposphere and thus underestimating the wave driving. In the GEOS V2 CCM the delayed breakup of the Antarctic vortex biases polar temperatures and trace gas distributions in the upper stratosphere in November and December.

Hurwitz, M. M.↗
NASA NTRS2015

Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA's Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE's coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

flood↗
NASA NTRS2017

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

D↗
NASA NTRS2017

Assessment of State-of-the-Art Dust Emission Scheme in GEOS

The GEOS modeling system has been extended with state of the art parameterization of dust emissions based on the vertical flux formulation described in Kok et al 2014. The new dust scheme was coupled with the GOCART and MAM aerosol models. In the present study we compare dust emissions, aerosol optical depth (AOD) and radiative fluxes from GEOS experiments with the standard and new dust emissions. AOD from the model experiments are also compared with AERONET and satellite based data. Based on this comparative analysis we concluded that the new parameterization improves the GEOS capability to model dust aerosols originating from African sources, however it lead to overestimation of dust emissions from Asian and Arabian sources. Further regional tuning of key parameters controlling the threshold friction velocity may be required in order to achieve more definitive and uniform improvement in the dust modeling skill.

Darmenov, Anton↗
NASA NTRS2018

The Central Role of Air Quality Observations in NASA's GEOS Composition Forecasting Model

The NASA GEOS composition forecast model (GEOS-CF) provides global, high-resolution (25 km) air quality forecasts in near-real time. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest compared to current, publicly-available global composition forecasts.Air quality observations are an indispensable tool to evaluate the model's ability to capture the strong temporal and spatial gradients of air pollutants across the globe. We show how comparisons against near-real time observations available through OpenAQ (www.openaq.org) demonstrate the model's overall success in reproducing surface concentrations of ozone, nitrogen dioxide, and PM2.5. This analysis also helps identifying current limitations of the model, for example over South America. The model-observation mismatches are most likely caused by uncertainties in the emissions data. Using the example of Rio de Janeiro, we show how the model skill can be improved by using local, high-resolution emission inventories in combination with air quality data.

Keller, Christoph A.↗
NASA NTRS2020

El Niño Related Tropical Land Surface Water and Energy Response in MERRA-2

Though El Niño events each have distinct evolutionary character they typically provide systematic large scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the MERRA-2 reanalysis and in a ten-member model AMIP ensemble. The lagged response (3-4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, while the AMIP ensemble reproduces some regional responses better than others. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather scale variability of the individual events, while retaining the systematic features across all events. We find that that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture leads to warmer surface temperature, more sensible heating and warming of the lower troposphere. While the composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, the regional and temporal response in any given event can be quite variable. The 2015-16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.

Michael G Bosilovich↗
NASA NTRSDate not supplied

PolarMERRA: A collaborative effort for NASA’s polar reanalysis

The polar atmosphere is often poorly represented in global models, adversely impacting analyses in these regions and forecasts on much larger scales. PolarMERRA is an initiative that brings together the modeling expertise from NASA’s global modeling and assimilation office (GMAO) and observational expertise from NASA’s Cryospheric sciences lab to assess and improve the representation of polar processes in the NASA’s modelling system in preparation for upcoming and next generation reanalyses. Model improvements in resolution, physics, and data assimilation have been guided by assessment of model skill in the polar regions. Preliminary results from a novel validation framework indicate a reduction in near surface air temperature, turbulent fluxes, and precipitation biases.

Chelsea Parker↗
NASA NTRS2022

A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

Wildfire is a global phenomenon that has dramatic effects on erosion and flood potential. On steep slopes, burned areas are more likely to experience significant overland flow during heavy rainfall leading to post fire debris flows (PFDFs). Previous work establishes methods for PFDF hazard assessment, often relying on regional-scale parameterizations with in-situ rainfall measurements to categorize hazard as a function of meteorological and surface properties. We present a globally scalable approach to extend the benefit these models provide to new areas. Our new model relies on publicly available satellite-based inputs with a global extent to provide first order hazard assessments of recently burned areas. Our results show it is possible to identify the conditions relevant for PFDF-initiation processes across a variety of physiographic settings. Improvements to satellite borne rainfall intensity data and increased availability of PFDF occurrence data worldwide are expected to enhance model skill and applicability further.

Wildfire↗
NASA NTRS2003

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

Chin, Mian↗
NASA NTRS2017

Effective Assimilation of SMAP Observations Using Statistical Techniques

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

Kolassa, J.↗
NASA NTRS1997

Initialization and Predictability of a Coupled ENSO Forecast Model

The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

Chen, Dake↗
NASA NTRS2023

Can Remotely Sensed Snow Disappearance Explain Seasonal Water Supply?

Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.

snow remote sensing↗
NASA NTRS2019

The Subseasonal Experiment (SubX): A Multi-Model Subseasonal Prediction Experiment

SubX is a multi-model subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced seventeen years of retrospective (re-) forecasts and more than a year of weekly real-time forecasts. The re-forecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation three weeks ahead of time in specific regions. The SubX multi-model ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden-Julian Oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated with skillful predictions of the MJO four weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones which can help emergency management and aid organizations to plan for disasters. (Capsule Summary) A research to operations project in service of developing better operational subseasonal forecasts.

Precipitation↗
NASA NTRS2012

The Second Phase of the Global Land Atmosphere Coupling Experiment (GLACE-2): Impact of Land Initialization on Subseasonal Forecasts

The recently-completed second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the multi-institutional numerical experiment is described, along with a determination and characterization of multi-model "consensus" skill. The models show modest but significant land-derived skill in predicting air temperatures out to two months, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.

Koster, Randal↗
NASA NTRS2015

Effects of Resolution and Spectral Nudging in Simulation the Effects of Wintertime Atmospheric River Landfalls in the Western US

Landfalling atmospheric rivers (ARs) play a crucial role in the climate of the US Pacific coast region as they are frequently related with heavy precipitation and flash flooding events. Thus, the capability of climate models to accurately simulate AR landfalls and their key hydrologic effects is an important practical concern for WUS, from flood forecasting to future water resources projections. In order to examine the effects of model configuration, including the resolution and spectral nudging, in simulating the climatology of key weather events in the conterminous US, a NASA team has performed a hindcast experiment using the GEOS5 global and the NU-WRF regional models for Nov 1999 - Oct 2010. This study examines the skill of these hindcasts, with different models and their configurations, in simulating key footprints of landfalling ARs in the WUS region. Using an AR-landfall chronology based on the vertically-integrated water vapor flux calculated from the MERRA2 reanalysis, we have analyzed the observed and simulated precipitation and temperature anomalies associated with wintertime AR landfalls along the US Pacific coast. Model skill is measured using metrics including regional means, a skill score based on correlations and mean-square errors, and Taylor diagrams in four WUS Bukovsky regions. Results show that the AR-related anomalies of precipitation is more reliable than of surface temperatures. Model skill also varies according to regions. The AR temperature anomalies are well simulated in most of the WUS region except PNW. For precipitation, simulations with finer spatial resolution tend to generate larger spatial variability and agree better with the PRISM data in most regions. Such a resolution dependence of spatial variability is not found for temperatures; e.g., the MERRA2 reanalysis often outperforms, with similar spatial variability and higher pattern correlations with the PRISM data, finer-resolution NU-WRF runs in simulating temperature variations within subregions. Results from this study will be summarized to assist future (regional) climate experiments for climate change impact assessments and developing adaptationmitigation strategies, the key elements of the National Climate Assessment.

Simulation↗
NASA NTRS1979

The effect of accuracy, conservation and filtering on numerical weather forecasting

Considerations leading to the numerical design of the GLAS fourth-order global atmospheric model are discussed, including changes recently introduced into the model. The computation time and memory requirements for the fourth-order model are similar to those of the present second-order GLAS model with the same 4 deg latitude, 5 deg longitude, and 9 vertical-level resolution. However, the fourth-order model forecast skill is significantly better than that of the current GLAS model, and after three days it is comparable to the 2.5 by 3 deg version of the GLAS model in the sea level pressure maps, and has less phase errors in the 500 mb maps.

Kalnay-Rivas, E.↗
NASA NTRS1985

Development and Testing of the Variable Vertical Resolution Fourth Order GCM

The vertical coordinate of the Fourth Order Model has been generalized so that the model can now run with an arbitrary number of vertical layers and so that the thicknesses of these layers can be arbitrarily specified (in the sigma coordinate). This Variable Vertical Resolution (VVR) version of the Fourth Order Model will soon replace the current production model. To assess the skill of the VVR model, it has been run with 9 equally spaced layers and compared with the current production model. In two Northern Hemispheric winter cases and one summer case, the two models were virtually identical in forecast skill for 6 to 7 days. After that the VVR model was slightly better in the winter cases and the production model was slightly better in the summer case. The only exception to this was that after 2 days the production model gave slightly more skillful 500 mb forecasts in the tropics for the summer case.

Helfand, H. M.↗