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NASA NTRS1999

Precipitation Recycling in the NASA GEOS Data Assimilation System

Analysis of precipitation recycling can improve the understanding of regional hydrologic anomalies, especially their evolution and maintenance. Diagnostic models of the recycling of precipitation and are applied to 15 years of the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). Recycled precipitation is defined as the fraction of precipitation within a given region that originated as surface evaporation from the same region. The focus of the present work is on the interannual variability of the central United States hydrologic cycle and precipitation recycling. The extreme years of 1988 (drought) and 1993 (flood) are compared with the 15 year base period mean annual cycle. The results indicate that recycling ratio (the amount of precipitation with a local source relative to the total precipitation) is greater in 1988 than both the base period mean and the 1993 season (with 1993 recycling ratio less than the mean). On the other hand, both the summers of 1988 and 1993 show less total recycled precipitation than the mean. The results also show that precipitation recycling may have been more important in the spring of 1993, when the region was primed for flooding, than the summer, when the sever flooding occurred. The diagnostic approaches to precipitation recycling suffer from some weaknesses. Numerical simulations and assimilation using passive tracers have the potential to provide more accurate calculations of precipitation recycling and the remote sources of water. This ability is being incorporated into the latest GEOS data assimilation system, and some preliminary results will be presented.

Bosilovich, Michael G.↗
NASA NTRS1999

Data Assimilation Into a Coupled Ocean Atmosphere Model: Application to the 1997-1998 El Nino

As part of JPL's ocean data assimilation effort to study ocean circulation and seasonal-interannual climate variability, sea level anomaly observed by TOPEX altimeter, together with sea surface temperature and wind stress data, are assimilated into a simple coupled ocean atmosphere model of the tropical Pacific. Model-data consistency is examined. Impact of the assimilation (as initialization) on El Nino Southern Oscillation (ENSO) forecasts is evaluated. The coupled model consists of a shallow water component with two baroclinic modes, an Ekman shear layer, a simplified mixed-layer temperature equation, and a statistical atmosphere based on dominant correlations between historical surface temperature and wind stress anomaly data. The adjoins method is used to fit the coupled model to the data over various six-month periods from late 1996 to early 1998 by optimally adjusting the initial state, model parameters, and basis functions of the statistical atmosphere. On average, the coupled model can be fitted to the data to approximately within the data and representation errors (5 cm, 0.5 C, and 10 sq m/sq m for sea level, surface temperature, and pseudo wind stress anomalies, respectively). The estimated fields resemble observed spatio-temporal structure reasonably well. Hindcasts/forecasts of the 1997/1998 El Nino initialized from forced estimated ocean states and parameters are much more realistic than those simply initialized from ocean states (see figure below). In particular, the ability of the model to produce significant warming beyond the initial state is dramatically improved. Parameter estimation, which compensates for some model errors, is found to be important to obtaining better fits of the model to data and to improving forecasts.

Lee, Tong↗
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation, Volume 43: Initial Evaluation of the Climate - MERRA-2

The years since the introduction of MERRA have seen numerous advances in the GEOS-5 Data Assimilation System as well as a substantial decrease in the number of observations that can be assimilated into the MERRA system. To allow continued data processing into the future, and to take advantage of several important innovations that could improve system performance, a decision was made to produce MERRA-2, an updated retrospective analysis of the full modern satellite era. One of the many advances in MERRA-2 is a constraint on the global dry mass balance; this allows the global changes in water by the analysis increment to be near zero, thereby minimizing abrupt global interannual variations due to changes in the observing system. In addition, MERRA-2 includes the assimilation of interactive aerosols into the system, a feature of the Earth system absent from previous reanalyses. Also, in an effort to improve land surface hydrology, observations-corrected precipitation forcing is used instead of model-generated precipitation. Overall, MERRA-2 takes advantage of numerous updates to the global modeling and data assimilation system. In this document, we summarize an initial evaluation of the climate in MERRA-2, from the surface to the stratosphere and from the tropics to the poles. Strengths and weaknesses of the MERRA-2 climate are accordingly emphasized.

MERRA-2↗
NASA NTRS2005

Documentation and Validation of the Goddard Earth Observing System (GEOS) Data Assimilation System, Version 4

This document describes the structure and validation of a frozen version of the Goddard Earth Observing System Data Assimilation System (GEOS DAS): GEOS-4.0.3. Significant features of GEOS-4 include: version 3 of the Community Climate Model (CCM3) with the addition of a finite volume dynamical core; version two of the Community Land Model (CLM2); the Physical-space Statistical Analysis System (PSAS); and an interactive retrieval system (iRET) for assimilating TOVS radiance data. Upon completion of the GEOS-4 validation in December 2003, GEOS-4 became operational on 15 January 2004. Products from GEOS-4 have been used in supporting field campaigns and for reprocessing several years of data for CERES.

Suarez, Max J.↗
NASA NTRS2018

The Atmosphere-Ocean Interface Layer of NASA's Goddard Earth Observing System Model and Data Assimilation System Volume 51

The Goddard Earth Observing System (GEOS) general circulation model (GCM) includes modules for sea surface temperature (SST) diurnal warming and cool-skin layers. To support the application of a coupled atmosphere-ocean data assimilation capability, the GCM needs to be flexible enough to support both coupled atmosphere ocean general circulation model (AOGCM) and atmosphere-only (AGCM) configurations, with only minor configuration changes at the user interface. This document presents a formulation of an atmosphere-ocean interface layer (AOIL) that serves this purpose. Previous work by Akella et al. (2017) described a version of a model for near-surface temperature variations, including both both diurnal warming and cool-skin effects, that has been used since 2017 in the near-real-time GEOS FP (forward processing) weather analysis and forecasting system. The diurnal cycle of SST in that version of the GEOS atmospheric data assimilation system (ADAS) undergoes a sharp decay in the late afternoon (local time). The updated AOIL presented here includes a modification of the similarity function used in the diurnal warming model. Results from offline model runs illustrate an improvement in the near-surface (less than 0:5m depth) diurnal cycle compared to the original formulation. The new formulation requires minimal parameter tuning, and the improvements are robust across long (several month) simulation periods. This new model formulation, however, retains some deficiences from the previous module, such as a small warm bias in calm wind conditions for water depths below 1m. Our future work would include surface salinification and sea-ice into the AOIL.

Koster, Randal D.↗
NASA NTRS1995

Technical Report Series on Global Modeling and Data Assimilation. Volume 7: Proceedings of the Workshop on the GEOS-1 Five-year Assimilation

The primary objective of the three-day workshop on results from the Data Assimilation Office (DAO) five-year assimilation was to provide timely feedback from the data users concerning the strengths and weaknesses of version 1 of the Goddard Earth Observing System (GEOS-1) assimilated products. A second objective was to assess user satisfaction with the current methods of data access and retrieval. There were a total of 49 presentations, with about half (23) of the presentations from scientists from outside of Goddard. The first two days were devoted to applications of data: studies of the energy diagnostics, precipitation and diabatic heating, hydrological modeling and moisture transport, cloud forcing and validation, various aspects of intraseasonal, seasonal, and interannual variability, ocean wind stress applications, and validation of surface fluxes. The last day included talks from the National Meteorological Center (NMC), the National Center for Atmospheric Research (NCAR), the Center for Ocean-Land-Atmosphere Studies (COLA), the United States Navy, and the European Center for Medium Range Weather Forecasts (ECMWF).

Suarez, Max J.↗
NASA NTRS2015

Completing the Feedback Loop: The Impact of Chlorophyll Data Assimilation on the Ocean State

In anticipation of the integration of a full biochemical model into the next generation GMAO coupled system, an intermediate solution has been implemented to estimate the penetration depth (1Kd_PAR) of ocean radiation based on the chlorophyll concentration. The chlorophyll is modeled as a tracer with sources-sinks coming from the assimilation of MODIS chlorophyll data. Two experiments were conducted with the coupled ocean-atmosphere model. In the first, climatological values of Kpar were used. In the second, retrieved daily chlorophyll concentrations were assimilated and Kd_PAR was derived according to Morel et al (2007). No other data was assimilated to isolate the effects of the time-evolving chlorophyll field. The daily MODIS Kd_PAR product was used to validate the skill of the penetration depth estimation and the MERRA-OCEAN re-analysis was used as a benchmark to study the sensitivity of the upper ocean heat content and vertical temperature distribution to the chlorophyll input. In the experiment with daily chlorophyll data assimilation, the penetration depth was estimated more accurately, especially in the tropics. As a result, the temperature bias of the model was reduced. A notably robust albeit small (2-5 percent) improvement was found across the equatorial Pacific ocean, which is a critical region for seasonal to inter-annual prediction.

MERRA-OCEAN↗
NASA NTRS2018

A New Atmosphere-Ocean Model for Studying Air-Sea Interactions and Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2012

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

forecasting↗
NASA NTRSDate not supplied

Insights Into the Hydrology of the Congo Peatlands Through Land Surface Modeling and Data Assimilation

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent. In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.

Sebastian Apers↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8 deg) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12 deg) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

Data Assimilation of Terrestrial Water Storage to Adjust Precipitation Fluxes

The Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. TWS is defined as the sum of groundwater, soil moisture, snow, surface water, ice and biomass water. Data assimilation of GRACE TWS observations has been shown to improve simulation of groundwater, streamflow, and snow water equivalent, and has also proven useful for drought monitoring and identifying human impacts on the water cycle. From a modeling perspective, the TWS components are defined as "prognostic hydrological states". Existing GRACE data assimilation schemes update these prognostic states directly. In this work, we propose an alternate approach in which precipitation fluxes are adjusted in order to achieve the desired change in the hydrological prognostic states. Limitations of such an approach include the assumption that all errors in TWS originate from errors in precipitation. Nonetheless, benefits comprise (1) the water balance is maintained, as opposed to having to add increments to the water budget components, (2) the model automatically determines how to distribute the updates among the TWS prognostic states, and (3) it is not necessary to know the exact time of the observation TWS, because the TWS change timing is determined by the precipitation forcing.

Girotto, Manuela↗
NASA NTRS2018

Consequences of Different Air-Sea Feedbacks on Ocean Using MITgcm and MERRA-2 Forcing: Implications for Coupled Data Assimilation Systems

Ocean surface flux estimates from atmospheric and oceanic reanalyses contain errors that compensate for inaccuracies in the respective atmosphere and ocean models used to generate these reanalyses. A conundrum for climate studies is the discrepancy between surface fluxes that minimize model-data differences for an atmosphere-only model vs surface fluxes that minimize model-data differences for an ocean model. As a first step towards a consistent coupled ocean-atmosphere data-assimilation (DA) system, we compare surface net heat flux from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to net heat flux from a state-of-the-art ocean state estimate, the Estimating the Circulation and Climate of the Ocean Version 4 (ECCO-v4). The possible impacts of the MERRA-2 and ECCO-v4 air-sea net heat flux difference in a coupled DA system were assessed using a set of experiments designed to imitate different “flavors” of a coupled DA system in an ocean-only setup. This was done by forcing the ECCO-v4 underlying ocean model - the Massachusetts Institute of Technology general circulation model (MITgcm) - with different sets of MERRA-2 fields and utilizing different forcing methods. By doing so we were able to turn off different air-sea feedbacks which, in a coupled DA setup, are partially muted by the constraining observations. The set of experiments, therefore, represents a range of active feedbacks in different “flavors” of coupled data-assimilation systems. For the period 1992–2011, MERRA-2 net heat flux has a global mean difference of -4.9 Wm(exp -2) relative to ECCO-v4. When MERRA-2 surface fields are used to force MITgcm, imbalances in the energy and the hydrological cycles of MERRA-2, which are directly related to the fact that MERRA-2 was created without an interactive ocean, propagate to the ocean. The experiment in which MITgcm is forced with MERRA-2 fluxes (MERRA-2-flux experiment) results in a 2.5°C global mean Sea Surface Temperature (SST) cooling, a 1m reduction in global mean sea level, and other drastic changes in the large scale ocean circulation relative to those resulting when the MITgcm is forced with the optimized ECCO-v4 net heat flux (the ECCO-v4 experiment itself). When MITgcm is forced with MERRA-2 state variables (MERRA-2-state experiment), the SST is somewhat restored to the observed SST, but the errors are shifted to the water cycle, resulting in a global mean sea level increase of 2.7 m. To further explore the pros and cons of these two approaches, we introduce a new intermediate forcing method in which the ocean is forced with turbulent fluxes but has a long wave feedback. This method, unlike MERRA-2 state, preserves the MERRA-2 water and salinity cycles, and it reduces the SST error compared to the MERRA-2-flux experiment, but the SST is not as good as that in the MERRA-2-state experiment. Our results have implications for ocean-model forcing recipes and clearly reveal the undesirable consequences of limiting the feedbacks in either these types of experiments or in coupled DA.

Ehud Strobach↗
NASA NTRS2018

Development of the GEOS-MIT gcm Atmosphere-Ocean Model for Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MIT-GCM (General Circulation Model)) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies. Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (approximately 1/8 degrees) configuration of the GEOS atmosphere to a latitude-longitude-cap-1080 (approximately 1/12 degrees) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS1980

The compatible balancing approach to initialization, and four-dimensional data assimilation

A unified theoretical approach to both the four-dimensional assimilation of asynoptic data and the initialization problem is attempted. This approach relies on the derivation of certain relationships between geopotential tendencies and tendencies of the horizontal velocity field in primitive-equation models of atmospheric flow. The approach is worked out and analyzed in detail for some simple barotropic models. Certain independent results of numerical experiments for the time-continuous assimilation of real asynoptic meteorological data into a complex, baroclinic weather prediction model are discussed in the context of the present approach. Tentative inferences are drawn for practical assimilation procedures.

Ghil, M.↗
NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗
NASA NTRS2019

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm(exp 3) cm(exp -3)), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

R Abolafia-Rosenzweig↗