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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

soil moisture↗
NASA NTRS2017

TRMM Microwave Imager (TMI) Updates for Final Data Version Release

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) will be updated to a final version within the next year. These updates are based on increased knowledge in recent years of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI version updates. This paper discusses four aspects of the TMI data product that will be improved: spacecraft attitude, calibration and quality control, along-scan bias corrections, and sensor pointing accuracy. These updates will be incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.

Tropical Rainfall Measuring Mission↗
NASA NTRS2017

Estimating Surface Soil Moisture from SMAP Observations Using a Neural Network Technique

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Soil Moisture Remote Sensing↗
NASA NTRS2017

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths can be known, drift in certain instruments and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are in observation-space data formats and have not been made easily available to reanalysis users.A test data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). We refer to this proof-of-concept data as the MERRA-2 Gridded Innovations and Observations (GIO). In this paper, we present the data format and its strengths and limitations with some initial testing and validation of the methodology.

SSMI↗
NASA NTRS2017

An 8-Year, High-Resolution Reanalysis of Atmospheric CO2 Mixing Ratios Based on OCO-2 and GOSAT-ACOS Retrievals

The NASA GMAO (Global Modeling and Assimilation Office) reanalysis blends OCO-2 (Orbiting Carbon Observatory 2) and GOSAT-ACOS (Greenhouse Gases Observing Satellite-Atmospheric Carbon Observations from Space) retrievals (top) with GEOS (Goddard Earth Observing System) model predictions (bottom) to estimate the full 3D (three-dimensional) state of CO2 every 3 hours (middle). This poster describes monthly atmospheric growth rates derived from the reanalysis and an application to aircraft data with the potential to aid bias correction.

GEOS-Carb↗
NASA NTRS2017

From LIMS to OMPS-LP: Limb Ozone Observations for Future Reanalyses

High vertical resolution and accuracy of ozone data from satellite-borne limb sounders has made them an invaluable tool in scientific studies of the middle and upper atmosphere. However, it was not until recently that these measurements were successfully incorporated in atmospheric reanalyses: of the major multidecadal reanalyses only ECMWF's (European Centre for Medium-Range Weather Forecasts') ERA (ECMWF Re-Analysis)-Interim/ERA5 and NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications-2) use limb ozone data. Validation and comparison studies have demonstrated that the addition of observations from the Microwave Limb Sounder (MLS) on EOS (Earth Observing System) Aura greatly improved the quality of ozone fields in MERRA-2 making these assimilated data sets useful for scientific research. In this presentation, we will show the results of test experiments assimilating retrieved ozone from the Limb Infrared Monitor of the Stratosphere (LIMS, 1978/1979) and Ozone Mapping Profiler Suite Limb Profiler (OMPS-LP, 2012 to present). Our approach builds on the established assimilation methodology used for MLS in MERRA-2 and, in the case of OMPS-LP, extends the excellent record of MLS ozone assimilation into the post-EOS era in Earth observations. We will show case studies, discuss comparisons of the new experiments with MERRA-2, strategies for bias correction and the potential for combined assimilation of multiple limb ozone data types in future reanalyses for studies of multidecadal stratospheric ozone changes including trends.

Wargan, K.↗
NASA NTRS2018

The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0

This document describes the implementation and usage of the Goddard Earth Observing System (GEOS) Hybrid Ensemble-Variational Atmospheric Data Assimilation System (Hybrid EVADAS). Its aim is to provide comprehensive guidance to users of GEOS ADAS interested in experimenting with its hybrid functionalities. The document is also aimed at providing a short summary of the state-of-science in this release of the hybrid system. As explained here, the ensemble data assimilation system (EnADAS) mechanism added to GEOS ADAS to enable hybrid data assimilation applications has been introduced to the pre-existing machinery of GEOS in the most non-intrusive possible way. Only very minor changes have been made to the original scripts controlling GEOS ADAS with the objective of facilitating its usage by both researchers and the GMAO's near-real-time Forward Processing applications. In a hybrid scenario two data assimilation systems run concurrently in a two-way feedback mode such that: the ensemble provides background ensemble perturbations required by the ADAS deterministic (typically high resolution) hybrid analysis; and the deterministic ADAS provides analysis information for recentering of the EnADAS analyses and information necessary to ensure that observation bias correction procedures are consistent between both the deterministic ADAS and the EnADAS. The nonintrusive approach to introducing hybrid capability to GEOS ADAS means, in particular, that previously existing features continue to be available. Thus, not only is this upgraded version of GEOS ADAS capable of supporting new applications such as Hybrid 3D-Var, 3D-EnVar, 4D-EnVar and Hybrid 4D-EnVar, it remains possible to use GEOS ADAS in its traditional 3D-Var mode which has been used in both MERRA and MERRA-2. Furthermore, as described in this document, GEOS ADAS also supports a configuration for exercising a purely ensemble-based assimilation strategy which can be fully decoupled from its variational component. We should point out that Release 1.0 of this document was made available to GMAO in mid-2013, when we introduced Hybrid 3D-Var capability to GEOS ADAS. This initial version of the documentation included a considerably different state-of-science introductory section but many of the same detailed description of the mechanisms of GEOS EnADAS. We are glad to report that a few of the desirable Future Works listed in Release 1.0 have now been added to the present version of GEOS EnADAS. These include the ability to exercise an Ensemble Prediction System that uses the ensemble analyses of GEOS EnADAS and (a very early, but functional version of) a tool to support Ensemble Forecast Sensitivity and Observation Impact applications.

Todling, Ricardo↗
NASA NTRS2018

Overview of Solar-Induced Chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, Cross-Mission Comparison, and Global Monitoring for GPP

The Orbiting Carbon Observatory-2 (OCO-2), launched in July 2014, is capable of measuring Solar-Induced chlorophyll Fluorescence (SIF), a functional proxy for terrestrial gross primary productivity (GPP). Although its primary mission is to measure the column-averaged mixing ratio of CO2 (Xco(sub 2)) to constrain global carbon source/sink distribution, one of the OCO-2 spectrometers allows for a robust SIF retrieval solely based on solar Fraunhofer lines. Here we present a technical overview of the OCO-2 SIF product, aiming to provide the scientific community guidance on best practices for data analysis, interpretation, and application. This overview consists of the retrieval algorithms, OCO-2 specific bias correction, retrieval uncertainty evaluation, cross-mission comparison with other existing SIF products, and a global-scale examination of the SIF-GPP relationship. With the initial three years of data (September 2014 onward), we compared OCO-2 SIF with retrievals from Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and examined its relationship with FLUXCOM and MODIS GPP datasets. Our results show that OCO-2 SIF, along with GOSAT products, closely resemble the mean spatial and temporal patterns of FLUXCOM GPP from regions to the globe. Compared with GOME-2, however, OCO-2 depicts a more realistic spatial contrast between the tropics and extra-tropics. The linear relationship between OCO-2 SIF and existing modeled GPP products diverges somewhat across biomes at the global scale, consistent with previous GOSAT or GOME-2 based findings when modeled GPP products were used, but in contrast to a consistent cross-biome SIF-GPP relationship obtained at flux tower sites with OCO-2 products. This contrast suggests a critical need to reconcile differences in diverse SIF and GPP products and the relationships among them. Overall, the OCO-2 SIF products are robust and valuable for monitoring the global terrestrial carbon cycle and for constraining the carbon source/sink strengths of the Earth system. Finally, insights are offered for future satellite missions optimized for SIF retrievals.

GPP↗
NASA NTRS2018

Long-Term Ozone Variability and Trends from Reanalyses

Stratospheric ozone has a profound impact on radiation and chemistry over various spatial and temporal scales. The evolution of stratospheric ozone over the 21st century, however, is not well understood, especially in the lower stratosphere. Highly vertically resolved ozone data from satellite-borne limb sounders have proved to be invaluable for studying ozone in the middle and upper stratosphere but it was not until recently that these measurements were successfully incorporated in atmospheric reanalyses. Validation and comparison studies have demonstrated that the addition of observations from the Microwave Limb Sounder (MLS) on EOS (Earth Observing System) Aura greatly improved the quality of ozone fields in MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) making these assimilated data sets more useful for scientific research. In this presentation we demonstrate that multidecadal lower-stratospheric ozone variability and trends can be derived from NASA's MERRA-2 reanalysis ozone. In particular, the reanalysis ozone bias-corrected using a chemistry model simulation as a transfer function agrees very well with recently reprocessed long ozonesonde records. Ozone trends in the lower stratosphere will be discussed in the context of recent findings (Ball et al., 2018) and interpreted in connection with long-term circulation changes in the lower stratosphere. Next, we show that the use of ozone data retrieved from the next generation OMPS (Ozone Mapping Profiler Suite) instruments, including the OMPS Limb Profiler, can successfully extend the reanalyses into the future allowing comprehensive monitoring of global ozone and interpretation of its evolution during the critical period of expected ozone recovery and climate change from increasing concentration of greenhouse gases.

ozone↗
NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

general circulation model (GCM)↗
NASA NTRS2018

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space, or their contribution to the eventual analyzed fields. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths are known, drift in certain instruments, cloud clearing, and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are saved in observation-space data formats and are not easily made available to reanalysis users.Here, we present an early version of a data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). In this paper, we present the data format (called MERRA-2 Gridded Innovations and Observations or GIO) and its strengths and limitations with some initial testing and validation of the methodology.

Bosilovich, Michael G.↗
NASA NTRS2019

Impact of GMI All-Sky Radiance Assimilation in the NASA GEOS Forecast System

The assimilation of cloud- and precipitation-affected ("all-sky") radiances has become an important focus of development at most numerical weather prediction centers. Efforts at the Global Modeling and Assimilation Office (GMAO) have focused on all-sky assimilation of GPM Microwave Imager (GMI) radiances, which became operational in the GEOS real-time production system in July 2018. Implementation of the all-sky capability required several upgrades to the GEOS hybrid 4D-EnVar assimilation infrastructure including the addition of control variables for cloud liquid, cloud ice, rain and snow, enhancements to the radiative transfer model, new hybrid background and observational error models, and modified quality control and bias correction procedures. This talk describes the impact of GMI all-sky radiance assimilation on GEOS analyses and forecasts as determined from examination of various metrics including statistics of background departures and analysis increments, forecast skill scores, and forecast sensitivity observation impact (FSOI) calculations. It is shown that in addition to the hydrometeors themselves, the initial wind, temperature and pressure fields all undergo significant dynamic adjustment in response to the analyzed cloud and precipitation features. Assimilation of GMI radiances leads to improved forecasts of lower tropospheric wind, temperature and humidity, especially in the tropics. The largest forecast improvements occur during the first 48 hours, with diminishing impact thereafter. However, combining GMI all-sky assimilation with improvements to the GEOS model physics as in the recent implementation of the real-time production system, extends these forecast improvements well in to the medium range. FSOI results based on a 24-hr moist global energy norm show that GMI radiances provide nearly uniform beneficial impact throughout the tropics, with more mixed impacts in the subtropics. While the overall impact of GMI is smaller than that of other, much more numerous microwave and hyperspectral infrared radiance types, its impact is among the largest of all radiance types on a per-observation basis.

Gelaro, Ron↗
NASA NTRS2019

Progress in Atmospheric Carbon Monitoring Using NASA's GEOS Model and Data from the OCO and GOSAT Missions

NASA's Global Modeling and Assimilation Office (GMAO) produces a variety of carbon products based the synthesis of satellite remote sensing data and outputs of the Goddard Earth Observing System (GEOS). This includes bottom-up surface fluxes due to fossil fuel emissions, biomass burning, terrestrial biospheric exchange, and ocean exchangeconstrained by measurements of nighttime lights, fire radiative power, normalized difference vegetation index, and ocean color. These fluxes are the basis of top-down estimates of carbon concentrations and fluxes. In particular, the GMAO system processes retrievals of column carbon dioxide (XCO2) from GOSAT and OCO-2 to produce a high-resolution, long-term global analysis of CO2 in three dimensions every 6 hours. Here, we discuss the potential applications of such products for satellite intercomparison and evaluation against independent, non-coincident data. We also highlight the ability to provide monthly global atmospheric growth rates inferred from the assimilated CO2 concentration product. Finally, we discuss the challenges facing such products including bias correction and the estimation and analysis of model transport errors.

Weir, B.↗
NASA NTRS2019

Assimilation of Aerosol Observations in the NASA GEOS Model

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. Starting with the assimilation of satellite based single-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM2.5. We will also present preliminary results related to the introduction of new aerosol data types in GEOS, including multi-spectral AOD retrievals.

AERONET↗
NASA NTRS2020

Dynamical Forecasts of Tropical Terrestrial Carbon Fluxes with the NASA S2S Retrospective Forecast System

Recent advances in the ability to predict climate anomalies at sub-seasonal to seasonal (S2S) timescales allow us to explore the possibility of forecasting carbon flux anomalies. Although carbon flux forecasting is a relatively new concept, it is potentially beneficial as it can help us better understand global and regional land-atmosphere carbon feedbacks associated with climate variations and can provide guidance for future field mission design. Here we evaluate the skill of forecasted terrestrial carbon anomalies generated from meteorological anomalies produced with the NASA Global Modeling and Assimilation Office (GMAO) S2S forecast system. We focus here on three representative time periods (the most recent 2015-2016 El Nino, 2011 La Nina, and 2014 as a neutral year), with each corresponding 9-month forecast comprising four ensemble members initialized in the preceding December. The meteorological variables produced by the GMAO forecast system were bias-corrected using a climatology derived from the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) before being used to drive a suite of offline simulations with the NASA Catchment-CN terrestrial biosphere model, a model that computes water-energy-carbon dynamics. Forecasts are evaluated by comparing against satellite-driven estimates of gross primary production (GPP) and inverse model estimates of net carbon flux that incorporate satellite carbon dioxide measurements. We find that the restrospectively predicted carbon fluxes in the tropics reasonably reproduce the signs and magnitudes of the observed anomalies between the 2015-2016 El Nino and the 2011 La Nina for both net flux and GPP. For instance, for the El Nino period, the magnitude of the forecasted negative GPP anomaly in the South American tropics (which undergoes anomalously warm and dry conditions) agrees with the observed GPP anomaly at leads of up to three or four months. Overall, this study demonstrates potential skill in the forecast of biospheric carbon fluxes a few months in advance, a capability that could contribute to attribution studies focusing on carbon flux variations and support innovative observation strategies in the future.

Lee, Eunjee↗
NASA NTRS2020

Assimilation of SMAP and ASCAT Soil Moisture Retrievals into the JULES Land Surface Model Using the Local Ensemble Transform Kalman Filter

A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

SMAP↗
NASA NTRS2020

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88° S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r(exp 2)=−0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r(exp 2)=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

Michael Studinger↗
NASA NTRSDate not supplied

Isolating the Impacts of Soil Moisture Initialization on Land Carbon Fluxes at the Sub-Seasonal to Seasonal Scale

Soil moisture, through its impact on transpiration, plays a significant role in controlling the productivity of terrestrial vegetation. Initial soil moisture conditions in a seasonal forecast may therefore affect the forecast of land carbon uptake. Here we investigate the relative impacts of soil moisture initialization and carbon reservoir initialization on forecasts of land carbon fluxes at the sub-seasonal to seasonal (S2S) scale. The bias-corrected, retrospective meteorology of NASA Global Modeling and Assimilation Office (GMAO)’s S2S ensemble forecast was used to force the stand-alone Catchment-CN model and thereby estimate terrestrial carbon responses out to nine lead months. Our results show that soil moisture initialization is a major contributor (approximately 44%) to the high global carbon uptake forecast skill seen during the first three lead months. The carbon reservoir initialization explains roughly another half of the monthly carbon forecast skill during this period and becomes relatively more important at longer leads (while the overall forecast skill decreases after the 3 lead month), suggesting a slower but longer-lasting influence of carbon reservoir initialization on carbon fluxes. Our results highlight the significance of a good soil moisture initialization for improved forecasts of carbon fluxes at leads of several months, further support for the usefulness of assimilating the satellite based soil moisture information into terrestrial biosphere models and of a short term carbon forecast to understand current events with often a lag in the availability of flux estimates.

Land carbon↗