Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult ...to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (1) along-the-stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases which often occur in flooding situations. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40 % more accurate (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along the Pee Dee River in South Carolina, and many other locations in the domain, where inflation is able to avoid filter divergence and, thereby, assimilate significantly more observations.
An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System ...(ENAAPS) at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data assimilation. The optimized ensemble system was compared to the Navy's current operational aerosol forecasting system, which makes use of NAVDAS-AOD (NRL Atmospheric Variational Data Assimilation System for aerosol optical depth), a 2-D variational data assimilation system. Overall, the two systems had statistically insignificant differences in root-mean-squared error (RMSE), bias, and correlation relative to AERONET-observed AOT. However, the ensemble system is able to better capture sharp gradients in aerosol features compared to the 2DVar system, which has a tendency to smooth out aerosol events. Such skill is not easily observable in bulk metrics. Further, the ENAAPS-DART system will allow for new avenues of model development, such as more efficient lidar and surface station assimilation as well as adaptive source functions. At this early stage of development, the parity with the current variational system is encouraging.
Abstract
An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth ...System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global,
$$\sim 1^\circ $$
∼
1
∘
resolution, 80 member ensemble span 2011–2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.
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The magnitude and persistence of land carbon (C) pools influence long‐term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is ...imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model‐variance‐inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead‐time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land‐atmosphere exchanges.
Plain Language Summary
The amount of carbon stored in vegetation and soils is an important control on how much carbon dioxide is in the atmosphere, and that influences future climate. Land surface models are used to simulate where this carbon is, but they are imperfect and there are often differences between model predictions and observations of the carbon stores. Here we describe a system that combines model predictions and observations and updates the modeled carbon stores so they are closer to the observations, considering uncertainty in both the model and the observations. We test our system at a location in New Mexico, United States, where we use observations from satellites of the amount of leaves on the vegetation and the amount of carbon stored in the vegetation. When we combine these observations with our land surface model there are large changes in the predicted amounts of stored carbon and the times of the year when the vegetation has the most leaves. These changes persist in the model after we stop updating it with observations, improving the model forecast.
Key Points
Data assimilation was used to initialize biomass and leaf area in the Community Land Model
Adaptive inflation was needed to give more weight to observations due to substantial discrepancies between model forecast and observations
Data assimilation reduces forecast error in a land surface model
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Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land ...Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.
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Ionospheric data assimilation and forecasting during storms Chartier, Alex T.; Matsuo, Tomoko; Anderson, Jeffrey L. ...
Journal of geophysical research. Space physics,
January 2016, 2016-01-00, 20160101, Volume:
121, Issue:
1
Journal Article
Peer reviewed
Open access
Ionospheric storms can have important effects on radio communications and navigation systems. Storm time ionospheric predictions have the potential to form part of effective mitigation strategies to ...these problems. Ionospheric storms are caused by strong forcing from the solar wind. Electron density enhancements are driven by penetration electric fields, as well as by thermosphere‐ionosphere behavior including Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances and changes to the neutral composition. This study assesses the effect on 1 h predictions of specifying initial ionospheric and thermospheric conditions using total electron content (TEC) observations under a fixed set of solar and high‐latitude drivers. Prediction performance is assessed against TEC observations, incoherent scatter radar, and in situ electron density observations. Corotated TEC data provide a benchmark of forecast accuracy. The primary case study is the storm of 10 September 2005, while the anomalous storm of 21 January 2005 provides a secondary comparison. The study uses an ensemble Kalman filter constructed with the Data Assimilation Research Testbed and the Thermosphere Ionosphere Electrodynamics General Circulation Model. Maps of preprocessed, verticalized GPS TEC are assimilated, while high‐latitude specifications from the Assimilative Mapping of Ionospheric Electrodynamics and solar flux observations from the Solar Extreme Ultraviolet Experiment are used to drive the model. The filter adjusts ionospheric and thermospheric parameters, making use of time‐evolving covariance estimates. The approach is effective in correcting model biases but does not capture all the behavior of the storms. In particular, a ridge‐like enhancement over the continental USA is not predicted, indicating the importance of predicting storm time electric field behavior to the problem of ionospheric forecasting.
Key Points
TEC observations are assimilated into a coupled thermosphere‐ionosphere model
Model biases are greatly reduced, but significant errors remain
One‐hour forecasts of midlatitude storm time ionospheric TEC are presented
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The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest ...mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.
Plain Language Summary
The Western United States is dominated by natural lands that play a critical role for carbon balance (e.g., trees, soils), water quality, and timber reserves. This region is also particularly vulnerable to tree death from drought, insect attack, and wildfires, thus requiring constant monitoring to assess its health. Traditional carbon monitoring techniques are usually not possible within mountainous terrain, thus we used satellite observations of leaf area and forest biomass to improve modeled simulations of the Western United States. When we accounted for observations of trees our modeled estimates showed reduced amounts of biomass and relatively small amounts of atmospheric CO2 transfer from the atmosphere to the land (the land absorbs carbon from the atmosphere through photosynthesis). Our best estimate of carbon absorbed by the land was much less than other modeled estimates. This suggests our method better accounted for the current conditions of the trees including death from fire, insect attack, and drought. Our modeled estimate of biomass and carbon balance across the Western United States can be improved further by considering more observations of the land surface related to soil moisture and soil carbon.
Key Points
Assimilating observations of biomass and leaf area reduces simulated biomass and projects a weak land carbon sink across the Western United States
Our estimate of carbon exchange contrasts with an independent FLUXCOM estimate that shows a significant carbon sink in the Western United States
Water cycle observations should be used to complement biomass observations to improve the spatial pattern of modeled carbon fluxes
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El Niño and climate change Trenberth, Kevin E.; Hoar, Timothy J.
Geophysical research letters,
1 December 1997, Volume:
24, Issue:
23
Journal Article
Peer reviewed
Open access
A comprehensive statistical analysis of how an index of the Southern Oscillation changed from 1882 to 1995 was given by Trenberth and Hoar 1996, with a focus on the unusual nature of the 1990–1995 El ...Niño‐Southern Oscillation (ENSO) warm event in the context of an observed trend for more El Niño and fewer La Niña events after the late 1970s. The conclusions of that study have been challenged by two studies which deal with only the part of our results pertaining to the length of runs of anomalies of one sign in the Southern Oscillation Index. They therefore neglect the essence of Trenberth and Hoar, which focussed on the magnitude of anomalies for certain periods and showed that anomalies during both the post‐1976 and 1990‐mid‐1995 periods were highly unlikely given the previous record. With updated data through mid 1997, we have performed additional tests using a regression model with autoregressive‐moving average (ARMA) errors that simultaneously estimates the appropriate ARMA model to fit the data and assesses the statistical significance of how unusual the two periods of interest are. The mean SOI for the post‐1976 period is statistically different from the overall mean at <0.05% and so is the 1990‐mid‐1995 period. The recent evolution of ENSO, with a major new El Niño event underway in 1997, reinforces the evidence that the tendency for more El Niño and fewer La Niña events since the late 1970s is highly unusual and very unlikely to be accounted for solely by natural variability.
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Continental-scale snow radiance assimilation (RA) experiments are conducted in order to improve snow estimates across snow and land-cover types in North America. In the experiments, the ensemble ...adjustment Kalman filter is applied and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T
B observations are assimilated into an RA system composed of the Community Land Model, version 4 (CLM4); radiative transfer models (RTMs); and the Data Assimilation Research Testbed (DART). The performance of two snowpack RTMs, the DenseMedia Radiative Transfer–Multi-Layers model (DMRT-ML), and the Microwave Emission Model of Layered Snowpacks (MEMLS) in improving snow depth estimates through RA is compared. Continental-scale snow estimates are enhanced through RA by using AMSR-E T
B at the 18.7- and 23.8-GHz channels 3% (DMRT-ML) and 2% (MEMLS) improvements compared to the cases using the 18.7- and 36.5-GHz channels and by considering the vegetation single-scattering albedo ω 2.5% (DMRT-ML) and 4.8% (MEMLS) improvements compared to the cases neglecting ω. The contribution of T
B of the vegetation canopy to T
B at the top of the atmosphere is better represented by considering ω in the RA system, and improvements in the resulting snow depth are evident for the forest land-cover type (about 5%–11%) and the taiga and alpine snow classes (about 5%–11% and 4%–8%, respectively), especially in the MEMLS case. Compared to the open-loop run (0.171-m snow depth RMSE), about 7% (DMRT-ML) and 10% (MEMLS) overall improvements of the RA performance are achieved.
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The Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data ...assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of parameter estimation is discussed. A CAM ensemble reanalysis has been generated for more than 15 yr. Atmospheric forcings from the reanalysis were required as input to generate an ocean ensemble reanalysis that provided initial conditions for decadal prediction experiments. The software enables rapid experimentation with differing sets of observations and state variables, and the comparison of different models against identical real observations, as illustrated by a comparison of forecasts initialized by interpolated ECMWF analyses and by DART/CAM analyses.
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