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Reichle, R.

Publications and source records attributed to Reichle, R..

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The Effects of an Improved Dynamic Vegetation Phenology Representation in a Global Land Surface Model

Evapotranspiration (ET) is a major driver of the interaction between the land surface and the atmosphere through its component mechanisms, including plant transpiration (T) and soil evaporation. To accurately capture land-atmosphere interactions in global Earth System Models, it is thus critical that the underlying land surface models accurately model both the land hydrology as well as the dynamic response of vegetation to environmental drivers. In an effort to introduce a more realistic vegetation representation, the NASA Catchment land surface model, which is part of the Goddard Earth Observing System (GEOS), has previously been merged with the carbon and nitrogen physics modules of the Community Land Model version 4, resulting in the new Catchment-CN model. Catchment-CN has inherited the advanced treatment of land surface hydrology of Catchment, but is additionally able to dynamically model the response of vegetation to environmental drivers, in contrast to the fixed vegetation climatology that was prescribed in Catchment. Recently, the parameterization of Catchment-CN vegetation has been augmented to better account for variability of vegetation responses to external forcings within existing plant functional types, and vegetation parameters have been calibrated against Moderate Resolution Imaging Spectrometer observations of the fraction of absorbed photosynthetically radiation. These efforts have led to a significant reduction in the RMSE of modeled photosynthetic activity with respect to observations.This presentation investigates the effect of the improved vegetation representation on the partitioning of ET within Catchment-CN. Specifically, we compare global maps of the T:ET ratio across different temporal scales in (1) the original Catchment model, (2) the original Catchment-CN model, and (3) the augmented and calibrated Catchment-CN model. The modeled T and ET estimates are compared against a comprehensive set of ground observations from various field studies, as well as independent global T:ET estimates from different ET algorithms provided in the context of the Water Cycle Observation Multi-mission Strategy ? Evapotranspiration (WACMOS-ET) initiative.

Kolassa, J.↗
NASA NTRS2019

Verification of the SMAP Level-4 Soil Moisture Analysis Using Rainfall Observations in Australia

Global, 3-hourly, 9-km resolution soil moisture estimates are available with a mean latency of ~2.5 days from the NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product. These estimates are based on the assimilation of SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Routine monitoring of the L4_SM system's assimilation diagnostics revealed occasionally large observation-minus-forecast Tb differences across eastern central Australia that resulted in large analysis increments (or adjustments) of the model forecast soil moisture. Because this region lacks in situ soil moisture measurements, we developed an alternative approach to assess the veracity of the soil moisture analysis increments in the L4_SM system. Using regional gauge-based precipitation data, we demonstrate that the L4_SM soil moisture increments are correlated with errors in the L4_SM precipitation forcing, suggesting that the SMAP Tb observations contribute valuable information to the L4_SM soil moisture estimates.

Reichle, R.↗
NASA NTRS2018

The SMAP Level-4 ECO Product - Phase 1: Improving Vegetation Simulations Through Observation-Driven Parameter Estimation

Simulations of hydrological fields as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 (L4) Eco-Hydrology (ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation (DA) algorithm. The DA system is developed for the NASA Goddard Earth Observing System version 5 (GEOS-5) Catchment-CN land surface model, which combines land hydrology components of the GEOS-5 Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa. For SMAP L4 ECO a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is implemented to improve the model's standalone skill. Next, the DA algorithm used to produce the SMAP L4 soil moisture product is adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. The DA system is further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component. In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Kolassa, J.↗
NASA NTRS2018

Assessing Version 4 of the SMAP L4_SM Data Product

Version 4 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product benefits from an improved land surface modeling system. Surface soil moisture is typically drier by several volumetric percent in Version 4 compared to Version 3, whereas root zone soil moisture is wetter in Version 4 in some regions and drier in others. Results from core validation site comparisons show that Version 4 of the L4_SM data product meets the accuracy requirement, which is formulated in terms of the RMSE after removal of the long-term mean difference (ubRMSE). The overall ubRMSE of the 3-hourly L4_SM data at the 9 km scale is 0.039 m3 m-3 for surface soil moisture and 0.029 m3 m-3 for root zone soil moisture, below the 0.04 m3 m-3 requirement. L4_SM surface and root zone soil moisture estimates are more skillful than model-only simulation estimates that are not informed by SMAP brightness temperature observations, with statistically significant improvements at the 5% level for surface soil moisture R and anomaly R values. Results from comparisons of the L4_SM product to in situ measurements from more than 400 sparse network sites corroborate the core validation site results.

Reichle, R.↗
NASA NTRS2018

Accounting for Static and Dynamic Open Water in the Modeling of SMAP Brightness Temperatures over Peatlands

Hydrological change in peatlands due to anthropogenic disturbance and global warming can release enormous amounts of greenhouse gas emissions. Passive microwave satellite observations are an opportunity to globally monitor these changes. Abundant static and dynamic open water surfaces in peatlands strongly affect observed brightness temperatures (Tb). Here, we account for these contributions in radiative transfer modeling using NASA's Goddard Earth Observing System Model version 5 (GEOS-5) static open water mask and, for the dynamic open water fraction, the simulated inundated area using a version of the GEOS-5 Catchment land surface model that has been modified for peatland areas (PEAT-CLSM). Modeled Tb is compared against two years of SMAP L-band Tb. Preliminary results indicate: (i) a bias reduction when including the static open water fraction in a simple RTM mixing model, and ii) significantly improved correlation between modeled and observed Tb when using land surface output from PEAT-CLSM instead of the operational CLSM.

passive microwave↗
NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗
NASA NTRS2017

Land Surface Precipitation and Hydrology in MERRA-2

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), provides global, 1-hourly estimates of land surface conditions for 1980-present at 50-km resolution. Outside of the high latitudes, MERRA-2 uses observations-based precipitation data products to correct the precipitation falling on the land surface. This paper describes the precipitation correction method and evaluates the MERRA-2 land surface precipitation and hydrology. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cyclingMERRA-2 system and the earlier MERRA reanalysis. Compared to 3-hourlyTRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere-land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from the earlier, offline MERRA-Land reanalysis. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. The MERRA-2 and MERRA-Land surface and root zone soil moisture skill vs. in situ measurements is slightly higher than that of ERA-Interim Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim Land. Seasonal anomaly R values against naturalized stream flow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim Land, somewhat lower for MERRA-Land, and lower still for MERRA.

MERRA-↗
NASA NTRS2017

SMAP Level 4 Surface and Root Zone Soil Moisture

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

SMAP↗
NASA NTRS2016

Overview of the SMAP Applications and the SMAP Early Adopters Program - NASA's First Mission-Directed Outreach Effort

Satellite data provide global observations of many of the earths system processes and features. These data are valuable for developing scientific products that increase our understanding of how the earths systems are integrated. The water, energy and carbon cycle exchanges between the land and atmosphere are linked by soil moisture. NASAs Soil Moisture Active Passive (SMAP) mission provides soil moisture and freeze thaw measurements from space and allows scientists to link the water energy and carbon cycles. In order for SMAP data to be best integrated into decision support systems, the mission has engaged with the stakeholder community since 2009 and has attempted to scale the utility of the data to the thematic societal impacts of the satellite product applications. The SMAP Mission, which launched on January 31, 2015, has actively grown an Early Adopter (EA) community as part of its applications effort and worked with these EAs to demonstrate a scaled thematic impact of SMAP data product in societally relevant decision support applications. The SMAP mission provides global observations of the Earths surface soil moisture, providing high accuracy, resolution and continuous global coverage. Through the Early Adopters Program, the SMAP Applications Team will spend the next 2 years after launch documenting and evaluating the use of SMAP science products in applications related to weather forecasting, drought, agriculture productivity, floods, human health and national security.

SMAP↗
NASA NTRS2016

SMAP Data Assimilation at the GMAO

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days.

Land Surface Model↗
NASA NTRS2011

Integrating Data from GRACE and Other Observing Systems for Hydrological Research and Applications

The Gravity Recovery and Climate Experiment (GRACE) mission provides a unique view of water cycle dynamics, enabling the only space based observations of water on and beneath the land surface that are not limited by depth. GRACE data are immediately useful for large scale applications such as ice sheet ablation monitoring, but they are even more valuable when combined with other types of observations, either directly or within a data assimilation system. Here we describe recent results of hydrological research and applications projects enabled by GRACE. These include the following: 1) global monitoring of interannual variability of terrestrial water storage and groundwater; 2) water balance estimates of evapotranspiration over several large river basins; 3) NASA's Energy and Water Cycle Study (NEWS) state of the global water budget project; 4) drought indicator products now being incorporated into the U.S. Drought Monitor; 5) GRACE data assimilation over several regions.

Rodell, M.↗
NASA NTRS2011

The Global Observing System in the Assimilation Context

Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues.

Reinecker, Michele M.↗
NASA NTRS2011

Evaluation of Global Observations-Based Evapotranspiration Datasets and IPCC AR4 Simulations

Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite ]based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddycovariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations ]based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

Mueller, B.↗
NASA NTRS2011

Predicting Hydrological Drought: Relative Contributions of Soil Moisture and Snow Information to Seasonal Streamflow Prediction Skill

in this study we examine how knowledge of mid-winter snow accumulation and soil moisture conditions contribute to our ability to predict streamflow months in advance. A first "synthetic truth" analysis focuses on a series of numerical experiments with multiple sophisticated land surface models driven with a dataset of observations-based meteorological forcing spanning multiple decades and covering the continental United States. Snowpack information by itself obviously contributes to the skill attained in streamflow prediction, particularly in the mountainous west. The isolated contribution of soil moisture information, however, is found to be large and significant in many areas, particularly in the west but also in region surrounding the Great Lakes. The results are supported by a supplemental, observations-based analysis using (naturalized) March-July streamflow measurements covering much of the western U.S. Additional forecast experiments using start dates that span the year indicate a strong seasonality in the skill contributions; soil moisture information, for example, contributes to kill at much longer leads for forecasts issued in winter than for those issued in summer.

Koster, R.↗
NASA NTRS2006

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Kim, E.↗