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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Performance of reanalysis and mesoscale models off the coast of Hawai'i

The eastern Hawai'i coast in the United States is characterized by considerable wind resource fuelled by persistent trade winds, making it an important area for energy research. The need is strong for reanalyses and higher-resolution regional simulations where observations have been historically limited, such as Hawai'i's offshore environments. However, studies using offshore observations in other parts of the world have shown that significant errors can occur in reanalyses and wind datasets, which can lead to inaccurate estimates of wind energy generation, payback periods, and extreme weather risks at project locations. The degree of such errors is influenced by a number of factors, including spatial resolution and the handling of processes within the planetary boundary layer (PBL). In this work, we provide a wind resource characterization from year-long lidar buoy measurements off the eastern coast of O'ahu, Hawai'i, an environment previously unobserved at the rotor level, and use the characterization to evaluate the performance of two simulation datasets. The O'ahu deployment location is meteorologically unique and less complex than land-based wind resource characterizations, being strongly characterized by trade winds with minimal land–atmosphere interaction influences. Despite the unique and fairly consistent meteorological conditions, we hypothesize that distinct simulation datasets will exhibit diverse ranges of errors similar to those that have been seen for other offshore locations. We find the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) to strongly underestimate observed wind speeds at the O'ahu location (bias = −1.54 m s −1 at a height of 140 m above sea level), while a regional Weather Research and Forecasting Model (WRF) simulation produced by the University of Hawai'i (UH-WRF) provides a significantly smaller wind speed bias (−0.25 m s −1 ), highlighting the value of running regional, higher-resolution simulations. The large bias noted for ERA5 is driven by significant underestimation of fast wind speeds (>9 m s −1 ), which the study site is largely characterized by, along with discontinuities in the ERA5 diurnal cycle. We also speculate that the relative sparsity of observations for data assimilation in this remote part of the world could influence the performance of ERA5 and that challenges with characterizing island effects could impact the performance of both datasets.

17 WIND ENERGY↗
DOE OSTI2026

AI/ML-Enhanced Wind Forecasts for Reducing Uncertainty in Prescribed Fire Planning

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Tropospheric Correction for InSAR Using Interpolated ECMWF Data and GPS Zenith Total Delay

To mitigate atmospheric errors caused by the troposphere, which is a limiting error source for spaceborne interferometric synthetic aperture radar (InSAR) imaging, a tropospheric correction method has been developed using data from the European Centre for Medium- Range Weather Forecasts (ECMWF) and the Global Positioning System (GPS). The ECMWF data was interpolated using a Stretched Boundary Layer Model (SBLM), and ground-based GPS estimates of the tropospheric delay from the Southern California Integrated GPS Network were interpolated using modified Gaussian and inverse distance weighted interpolations. The resulting Zenith Total Delay (ZTD) correction maps have been evaluated, both separately and using a combination of the two data sets, for three short-interval InSAR pairs from Envisat during 2006 on an area stretching from northeast from the Los Angeles basin towards Death Valley. Results show that the root mean square (rms) in the InSAR images was greatly reduced, meaning a significant reduction in the atmospheric noise of up to 32 percent. However, for some of the images, the rms increased and large errors remained after applying the tropospheric correction. The residuals showed a constant gradient over the area, suggesting that a remaining orbit error from Envisat was present. The orbit reprocessing in ROI_pac and the plane fitting both require that the only remaining error in the InSAR image be the orbit error. If this is not fulfilled, the correction can be made anyway, but it will be done using all remaining errors assuming them to be orbit errors. By correcting for tropospheric noise, the biggest error source is removed, and the orbit error becomes apparent and can be corrected for

Webb, Frank H.↗
NASA NTRS2014

Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

This paper seeks to characterize the temporal spacing errors resulting from the use of Interval Management (IM) algorithms. The focus of the current paper is IM concepts and algorithms that realize a specified temporal spacing between a Target aircraft and an Ownship aircraft at the runway threshold. The paper presents an IM algorithm consisting of the following four modules: (i) Target-Landing-Time Estimation Module, (ii) Ownship-Landing-Time Estimation Module, (iii) Ownship Speed Command Computation Module, and (iv) Ownship Thrust Command Computation Module. The overall guidance module is evaluated on a simulation that models aircraft point-mass dynamics, bank-angle auto-pilot dynamics, pitch-axis auto-pilot dynamics, and engine lag dynamics. The simulation environment also consists of actual atmospheric forecasts and realistic spatio-temporally correlated wind uncertainty models. Results obtained from single case simulation as well as Monte-Carlo simulations are presented in the paper. The modeled scenario consisted of an A320 Target equipped with “Lateral Navigation”/“Vertical Navigation” (LNAV/VNAV) capabilities followed by an A320 Ownship equipped with the IM algorithm. Both aircraft fly the BIGSUR route to SFO airport using a RAP-13 1-hr wind forecast. 500 Monte-Carlo simulations were conducted with realistic wind uncertainty models. The IM algorithm for this case is seen to have a 90% probability landing time error range of 5.9 seconds, compared to the no-IM solution, which has a 90% probability landing time error range of 33.4 seconds.

Bai, Xiaoli↗
DOE OSTI2022

A Statistical Interpolation Code for Ocean Analysis and Forecasting

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .

Srinivasan, Ashwanth↗
DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Gravitational waves and galaxies cross-correlations: a forecast on GW biases for future detectors

ABSTRACT Gravitational waves (GWs) have rapidly become important cosmological probes since their first detection in 2015. As the number of detected events continues to rise, upcoming instruments like Einstein Telescope (ET) and Cosmic Explorer (CE) will observe millions of compact binary mergers. These detections, coupled with galaxy surveys by instruments such as the Dark Spectroscopic Energy Instrument (DESI), Euclid, and the Vera Rubin Observatory, will provide unique information on the large-scale structure of the universe by cross-correlating GWs with the distribution of galaxies hosting them. In this paper, we focus on how cross-correlations constrain the clustering bias of GWs emitted by the coalescence of binary black holes (BBHs). This parameter links BBHs to the underlying dark matter distribution, hence informing us how they populate galaxies. Using a multitracer approach, we forecast the precision of these measurements under different survey combinations. Our results indicate that current GW detectors will have limited precision, with measurement errors as high as $\displaystyle \sim 50~{{\ \rm per\ cent}}$. However, third-generation detectors like ET, when cross-correlated with Legacy Survey of Space and Time (LSST) data, can improve clustering bias measurements to within 2.5 per cent. Furthermore, we demonstrate that these cross-correlations can enable a per cent-level measurement of the magnification lensing effect on GWs. Despite this, there is a degeneracy between magnification and evolution biases, which hinders the precision of both. This degeneracy is most effectively addressed by assuming knowledge of one bias or targeting an optimal redshift range of $\displaystyle 1 \lt z \lt 2.5$. Our analysis opens new avenues for studying the distribution of BBHs and testing the nature of gravity through large-scale structure.

Zazzera, Stefano (ORCID:0000000158979221)↗
NASA NTRS2004

GMS-based"Future Time" Rainfall Data Assimilation for Mesoscale Weather Prediction over Korean Peninsula and Future Prospects with GPM Satellite Measurements

This study examines the use of satellite-derived nowcasted (short-term forecasted) rainfall over 3-hour time periods to gain an equivalent time increment in initializing a nonhydrostatic mesoscale model used for predicting convective rainfall events over the Korean peninsula. Infrared (IR) window measurements from the Japanese Geostationary Meteorological Satellite (GMS) are used to specify latent heating for a spinup period of the model - but in future time -- thus initializing in advance of actual time in the framework of a prediction scenario. The main scientific objective of the study is to investigate the strengths and weaknesses of this approach insofar as data assimilation, in which the nowcasted assimilation data are derived independently of the prognostic model itself. Although there have been various recent improvements in formulating the dynamics, thermodynamics, and microphysics of mesoscale models, as well as computer advances which allow the use of high resolution cloud-resolving grids and explicit latent heating over regional domains, spinup remains at the forefront of unresolved mesoscale modeling problems. In general, non-realistic spinup limits the skill in predicting the spatial-temporal distribution of convection and precipitation, primarily in the early hours of a. forecast, stemming from standard prognostic variables not representing the initial diabatic heating field produced by the ambient convection and cloud fields. The long-term goal of this research is to improve short-range (12-hour) quantitative precipitation forecasting (QPF) over the Korean peninsula through the use of innovative data assimilation methods based on geosynchronous satellite measurements. As a step in th~s direction, a non-standard data assimilation experiment in conjunction with GMS-retrieved nowcasted rainfall information introduced to the mesoscale model is conducted. The 3-hourly precipitation forecast information is assimilated through nudging the associated diabatic heating during the early stages of a forecast period. This procedure is expected to enhance details in the moisture field during model integration, and thus improve spinup performance, assuming the errors in the future time latent heating data ate less than intrinsic model background errors.

Smith, Eric A.↗
NASA NTRS2009

Severe Weather and Weak Waterspout Checklist in MIDDS

The goal of this task was to migrate the functionality of the AMU web-based Severe Weather Forecast Decision Aid and the 45 WS Weak Waterspout Checklist to MIDDS, the operational data ingest and display system of the 45 WS. Forecasting the occurrence and timing of warm season severe weather and weak waterspouts is challenging for 45 WS operational personnel. These interactive tools assist forecasters in determining the probability of issuing severe weather watches and warnings for the day. MIDDS is able retrieve many of the needed parameter values for the worksheet automatically. The AMU was able to develop user-friendly tools in MIDDS for both of these tools using McBASI coded programs. The tools retrieve needed values from MIDDS automatically, and require the forecaster to answer a few subjective questions. Both tools were tested and previewed to the 45 WS on MIDDS. In their previous forms, the forecasters enter values into both tools manually to output a threat index. Making these tools more automatic will reduce the possibility of human error and increase efficiency.

Wheeler, Mark M.↗
DOE OSTI2023

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Abstract It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Assimilation of MLS and OMI Ozone Data

Ozone data from Aura Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) were assimilated into the ozone model at NASA's Global Modeling and Assimilation Office (GMAO). This assimilation produces ozone fields that are superior to those from the operational GMAO assimilation of Solar Backscatter Ultraviolet (SBUV/2) instrument data. Assimilation of Aura data improves the representation of the "ozone hole" and the agreement with independent Stratospheric Aerosol and Gas Experiment (SAGE) III and ozone sonde data. Ozone in the lower stratosphere is captured better: mean state, vertical gradients, spatial and temporal variability are all improved. Inclusion of OMI and MLS data together, or separately, in the assimilation system provides a way of checking how consistent OMI and MLS data are with each other, and with the ozone model. We found that differences between OMI total ozone column data and model forecasts decrease after MLS data are assimilated. This indicates that MLS stratospheric ozone profiles are consistent with OMI total ozone columns. The evaluation of error characteristics of OMI and MLS ozone will continue as data from newer versions of retrievals becomes available. We report on the initial step in obtaining global assimilated ozone fields that combine measurements from different Aura instruments, the ozone model at the GMAO, and their respective error characteristics. We plan to use assimilated ozone fields in estimation of tropospheric ozone. We also plan to investigate impacts of assimilated ozone fields on numerical weather prediction through their use in radiative models and in the assimilation of infrared nadir radiance data from NASA's Advanced Infrared Sounder (AIRS).

Stajner, I.↗
NASA NTRS2007

Improved Atmospheric Soundings and Error Estimates from Analysis of AIRS/AMSU Data

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Three very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control; and 3) development of an accurate AIRS only cloud clearing and retrieval system. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions, without the need for microwave observations in the cloud clearing step as has been done previously. In this methodology, longwave C02 channel observations in the spectral region 700 cm-' to 750 cm-' are used exclusively for cloud clearing purposes, while shortwave C02 channels in the spectral region 2195 cm-' to 2395 cm-' are used for temperature sounding purposes. The new methodology for improved error estimates and their use in quality control is described briefly and results are shown indicative of their accuracy. Results are also shown of forecast impact experiments assimilating AIRS Version 5.0 retrieval products in the Goddard GEOS 5 Data Assimilation System using different quality control thresholds.

Susskind, Joel↗
NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, S.-H.↗
NASA NTRS2015

The Impact of Assimilating Precipitation-affected Radiance on Cloud and Precipitation in Goddard WRF-EDAS Analyses

High-frequency TMI and AMSR-E radiances, which are sensitive to precipitation over land, are assimilated into the Goddard Weather Research and Forecasting Model- Ensemble Data Assimilation System (WRF-EDAS) for a few heavy rain events over the continental US. Independent observations from surface rainfall, satellite IR brightness temperatures, as well as ground-radar reflectivity profiles are used to evaluate the impact of assimilating rain-sensitive radiances on cloud and precipitation within WRF-EDAS. The evaluations go beyond comparisons of forecast skills and domain-mean statistics, and focus on studying the cloud and precipitation features in the jointed rainradiance and rain-cloud space, with particular attentions on vertical distributions of height-dependent cloud types and collective effect of cloud hydrometers. Such a methodology is very helpful to understand limitations and sources of errors in rainaffected radiance assimilations. It is found that the assimilation of rain-sensitive radiances can reduce the mismatch between model analyses and observations by reasonably enhancing/reducing convective intensity over areas where the observation indicates precipitation, and suppressing convection over areas where the model forecast indicates rain but the observation does not. It is also noted that instead of generating sufficient low-level warmrain clouds as in observations, the model analysis tends to produce many spurious upperlevel clouds containing small amount of ice water content. This discrepancy is associated with insufficient information in ice-water-sensitive radiances to address the vertical distribution of clouds with small amount of ice water content. Such a problem will likely be mitigated when multi-channel multi-frequency radiances/reflectivity are assimilated over land along with sufficiently accurate surface emissivity information to better constrain the vertical distribution of cloud hydrometers.

WRF-EDAS Analyses↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
NASA NTRS2012

Comparison of Dst Forecast Models for Intense Geomagnetic Storms

We have compared six disturbance storm time (Dst) forecast models using 63 intense geomagnetic storms (Dst <=100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (Delta Dst(sub min)) and the difference in the absolute value of Dst minimum time (Delta t(sub Dst)) between the observed and the predicted. As a result, we found that the model by Temerin and Li gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the Delta Dst(sub min) of 7.7 nT, and the absolute value of Delta t(sub Dst) of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee is better than the other models for the events having 100 <= Dst < 200 nT, and three recent models (the model of Wang et al., the model of Temerin and Li, and the model of Boynton et al.) are better than the other three models for the events having Dst <= 200 nT.

Geomagnetic Storms↗
NASA NTRS2021

A Phenomenon-Based Decomposition of Model-Based Estimates of Boreal Winter ENSO Variability

Climate models are now routinely being used to simulate and predict climate variability on time scales ranging from sub-seasonal to seasonal and longer. As such, there are now long histories of such simulations and predictions spanning multiple decades and multiple ensemble members, both of which are crucial for separating climate signal from climate noise. A key focus of such runs has been the El Niño-Southern Oscillation (ENSO), spurred by recent improvements in our ability to predict such events, though questions remain as to how well climate models do beyond simply always predicting the “canonical” atmospheric response to an ENSO event—something simple statistical models already do reasonably well. This is a critical issue that needs addressing, given the importance of event-to-event differences for predicting regional impacts of ENSO teleconnections, and the need to justify the expense of running sophisticated climate models. Unfortunately, current diagnostic tools are not well suited for quantifying the different sources of variability associated with specific phenomena such as ENSO. More generally, while much effort has focused on addressing model bias, less has been done to address errors in second moment statistics—an issue whose importance is gaining increased attention particularly as we build climate prediction systems capable of taking advantage of forecasts of opportunity—a capability that requires reliable estimates of forecast uncertainty. In this report, we outline a phenomenon-based statistical decomposition of climate variance(in essence a detailed variance budget)that is specifically tailored to address the above questions by separating the variability (both the signal and noise) into that tied to the long-term average impact of a particular phenomenon(the composite mean) and the event-to-event(E2E) variability about the composite mean. In addition, we provide related decompositions of the correlations that allow us to quantify how much of the agreement with observations (the skill) comes from the composite mean as opposed to from the E2Evariability. As an example, we present the results of such a decomposition for ENSO based on simulations with the GEOS atmospheric general circulation model (AGCM), with a focus on the monthly mean impacts over North America during boreal winter(December –March). Here we take advantage of existing GEOS AGCM simulations that were produced as companion simulations to MERRA-2for the period 1980-2016. Comparisons are made throughout with MERRA-2.

ENSO↗
DOE OSTI2022

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5

Abstract. There is growing interest in data-driven weather prediction (DDWP), e.g., using convolutional neural networks such as U-NET that are trained on data from models or reanalysis. Here, we propose three components, inspired by physics, to integrate with commonly used DDWP models in order to improve their forecast accuracy. These components are (1) a deep spatial transformer added to the latent space of U-NET to capture rotation and scaling transformation in the latent space for spatiotemporal data, (2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and (3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals. To show the benefit and feasibility of each component, we use geopotential height at 500 hPa (Z500) from ERA5 reanalysis and examine the short-term forecast accuracy of specific setups of the DDWP framework. Results show that the spatial-transformer-based U-NET (U-STN) clearly outperforms the U-NET, e.g., improving the forecast skill by 45 %. Using a sigma-point ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we show that stable, accurate DA cycles are achieved even with high observation noise. This DDWP+DA framework substantially benefits from large (O(1000)) ensembles that are inexpensively generated with the data-driven forward model in each DA cycle. The multi-time-step DDWP+DA framework also shows promise; for example, it reduces the average error by factors of 2–3. These results show the benefits and feasibility of these three components, which are flexible and can be used in a variety of DDWP setups. Furthermore, while here we focus on weather forecasting, the three components can be readily adopted for other parts of the Earth system, such as ocean and land, for which there is a rapid growth of data and need for forecast and assimilation.

54 ENVIRONMENTAL SCIENCES↗