Temperature Assimilation Research Articles

Bayesian Inference for Estimating Heat Sources Through Temperature Assimilation

Abstract This paper utilizes a Bayesian inference framework to address the two-dimensional (2D) steady-state heat conduction problem, focusing on the estimation of unknown distributed heat sources in a thermally conducting medium with uniform conductivity. The goal is to infer the locations, strength, and shape of heaters by assimilating temperature data in Euclidean space, employing a Fourier series to represent each heater's shape. The Markov Chain Monte Carlo (MCMC) method, incorporating the random-walk Metropolis–Hasting (MH) algorithm and parallel tempering, is utilized for posterior distribution exploration in both unbounded and wall-bounded domains. It is found that multiple solutions arise in cases where the number of temperature sensors is less than the number of unknown states. Moreover, smaller heaters introduce greater uncertainty in estimated strength. To address the challenge of estimating the heater's strength and shape simultaneously due to their strong correlation, our method incorporates sharp priors on one to ensure accurate and feasible solutions of the other. The diffusive nature of heat conduction smooths out any deformations in the temperature contours, especially in the presence of multiple heaters positioned near each other, impacting convergence. In wall-bounded domains with Neumann boundary conditions, the inference of heater parameters tends to be more accurate than in unbounded domains.

A microwave temperature sounder (MWTS)–microwave humidity sounder fusion cloud detection method for land data from the FengYun‐3D MWTS and its impact on error estimation

AbstractThe presence of clouds greatly disrupting the estimation of observation errors in satellite data. Therefore, cloud detection is a necessary step in satellite radiance data assimilation, not only to identify clear sky data and cloudy data, but also, more importantly, to accurately specify observation errors in different scenarios during all‐sky data assimilation. However, the assimilation of the microwave temperature sounder‐2 (MWTS‐2) onboard the FengYun‐3D satellite has been restricted by the low accuracy of cloud detection due to the absence of low‐frequency channels, especially for land areas where complex underlying surface brings more difficulties to the identification of cloudy data. The microwave humidity sounder‐2 (MWHS‐2), which is designed for water vapor sounding, is usually equipped on the same satellite as MWTS‐2. The sensitivity of the MWHS‐2 to water vapor enables it to provide more accurate information about clouds over land. Based on the advantage of the two instruments measuring on the same platform, an effective cloud detection method for MWTS‐2 data over land is established in this study after collocating the data of the MWTS‐2 and MWHS‐2. The results show that the new method achieves a cloud detection rate exceeding 75%, with a significantly reduced false detection rate compared with the traditional scattering index method, which is decreased from 40% to approximately 26%. More importantly, in non‐precipitating cloud regions, the cloud index built by the new method has a significant linear correlation with the difference between observed and simulated brightness temperatures (OMB) of MWTS‐2 data. Statistical analysis reveals that the cloud index can be used to effectively rectify the systematic OMB bias caused by those clouds in non‐precipitation cloud areas, which demonstrates the promising application of this cloud index in all‐sky MWTS‐2 data assimilation over land.

Surface temperature assimilation improving geostationary meteorological satellite surface-sensitive brightness temperature simulations over land

This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (OB) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs.

Improving inland precipitation forecast in China through data assimilation of microwave temperature sounding data from a three‐orbit constellation

AbstractMicrowave temperature sounders onboard polar‐orbiting satellites can provide global observation data twice a day, supplying a large amount of temperature information for global data assimilation and serving as a crucial instrument to improve operational numerical forecasts. However, regional numerical forecasts are still subject to a lack of polar‐orbiting satellite data within regional model domains, and even multiple polar‐orbiting satellites may simultaneously miss measurements. Establishing a three‐orbit observation system of polar‐orbiting satellites is crucial to improve the spatiotemporal coverage of polar‐orbiting satellite data. In this study, we investigate the impact of assimilating microwave temperature sounding data from a three‐orbit constellation on precipitation forecasts in inland China based on the data from the US afternoon‐orbit satellite NOAA‐19, the European morning‐orbit satellite Meteorological Operational satellite‐A and the Chinese early‐morning‐orbit satellite Fengyun‐3E (FY‐3E) launched recently. The research results indicate that there are data gaps at 0600 and 1800 UTC in the East Asian region only for the morning‐orbit and afternoon‐orbit satellite observations. The FY‐3E satellite can provide additional microwave temperature sounding observations over the eastern region of China, thus partially compensating for the gap in polar‐orbiting satellite data in China. Moreover, the additional assimilation of the FY‐3E data can further improve numerical forecasts, effectively adjusting the spatial structure and eastward movement of the weather system, thereby considerably increasing the prediction accuracy of rainfall location and intensity. Rolling‐prediction results show that the data from the three‐orbit constellation provide a stable and notable improvement in precipitation forecasts in inland China, especially for forecasts longer than nine hours and amounts of rainfall below 10 mm. These research findings provide valuable insights for optimizing the assimilation application of polar‐orbiting satellite data in regional numerical forecasts.

Assessment of long-term multisource surface and subsurface soil moisture products and estimate methods on the Tibetan Plateau

This research is the first attempt to extensively assess the accuracy of commonly used surface and subsurface soil moisture (SM) datasets in reproducing temporal dynamics and trend changes SM observations for a long period on the Tibetan Plateau (TP). To this end, 12 satellite-based and 10 model-based SM products are evaluated using a 10-year upscaled SM dataset obtained from two in situ monitoring networks situated respectively in humid and arid climate zones over the TP. Moreover, the ability of using the outperforming satellite-based surface SM data in combination with cumulative distribution function (CDF) matching and exponential filter (ExpF) methods to improve model-based SM profile estimations is investigated for the first time. Among the 12 satellite-based products, SMAP_L3 provides the best surface SM retrievals, with the DCA and SCA-V products showing superior performance in the humid and arid areas, respectively. Consequently, the harmonized products produced based on the SMAP data (i.e., SMOSMAP and NNsm) also give relatively higher accuracy. It is surprised to find that the latest version of both CCI passive and active products presents degraded performance compared to its predecessor (i.e. v5.3). Among the 10 model-based products, SMAP_L4 with assimilation of L-band brightness temperature (TB) measurements provides relatively better estimations of surface and subsurface SM on the TP. Above findings highlight the superiority of utilizing the L-band TB measurements especially from the SMAP satellite to generate surface SM and improve the model-based SM estimations via data assimilation. Alternatively, it is further demonstrated that incorporating surface SM data from SMAP_L3, in conjunction with both CDF matching and ExpF methods, significantly enhances the precision of model-based SM estimations. This may open up a new possibility for improving the model-based SM profile data across the TP.

Assimilation of IASI temperature and humidity retrievals with scene‐dependent observation operators in clear‐sky scenes

AbstractThe potential of assimilating Infrared Atmospheric Sounding Interferometer (IASI) temperature and humidity retrievals with their scene‐dependent observation operators, a product developed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), has been investigated in the European Centre for Medium‐Range Weather Forecasts (ECMWF) system. The results are compared with the corresponding radiance assimilation impact. The experiments are done in a depleted observing‐system framework to emphasise the impact originating from the new data. The focus in the first assimilation experiments has been on retrievals over sea and in clear‐sky scenes, as they have high and homogeneous quality. The results demonstrate a clear statistically significant positive impact on temperature, humidity, and wind forecasts. However, the impact is somewhat smaller in magnitude for the retrieval assimilation compared with the radiance assimilation above 700 hPa.

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

Zircon double-dating, trace element and O isotope analysis to decipher late Pleistocene explosive-effusive eruptions from a zoned ocean-island magma system, Ascension Island

In this first detailed study of zircon from Ascension Island, South Atlantic, we take a novel approach combining trace element and O isotope compositional data with double-dating (disequilibrium 238U–230Th and (U–Th)/He) to decipher timescales and dynamics of magmatic processes. The Echo Canyon (EC) sequence comprises small-volume explosive-effusive eruptions of trachyte that tapped a compositionally zoned magma system. Associated volcanic hazards may be constrained from the age of volcanism, duration of magma storage, and magma source and plumbing system character. Zircon U–Th–Pb dating of lithic lava clasts has revealed recurrent evolved volcanism at 1.34 and 0.6 Ma, and 95 ka. The (U–Th)/He zircon cooling ages indicate that most of the EC explosive-effusive sequence erupted in a brief episode at ca. 95 ka. Additionally, uniform 238U–230Th zircon crystallisation ages suggest moderately protracted magma storage with melt present at depth for at most 103–104 years before eruption. The enriched character of zircon trace element compositions, relative to MORB, in the absence of a continental crustal signature in the oxygen isotope values (δ18O range 2.67–5.63‰), suggests the presence of an enriched component in the EC magma source. Furthermore, low δ18O zircon compositions imply assimilation of high temperature hydrothermally altered country rock by the source magma. The mineral assemblage in crystal-poor pumices indicates equilibrium storage conditions: zircon saturation and Ti-in-zircon crystallisation temperatures are consistent with alkali feldspar-melt temperatures. Significantly, zircon crystals were preserved both as macrocryst inclusions and in the groundmass of EC explosive and effusive deposits. These rocks preserve evidence of magma evolution by fractional crystallisation. This process led to pre-eruptive compositional stratification, which is evidenced in the range of whole-rock major and trace element compositions and zircon Zr/Hf values. Notably, zircon crystallisation and cooling ages derived from pumice, lava, and accidental lithic lava clasts in highly explosive pyroclastic deposits, have revealed episodes of evolved magmatism that would otherwise have gone undetected. In addition, the zircon trace element and isotope compositions, in combination with the range of crystallisation ages, evidence progressively deeper tapping of less evolved magma stored in discrete lenses. Thus, a combined zircon geochronological-geochemical approach can place constraints on the ca. 0.6 Ma recurrence of past explosive-effusive pulses of millennial to decamillennial duration and their enriched magma sources. This information is relevant for assessing hazards and informing monitoring and forecasting efforts to assist in managing associated risks for small ocean island volcanoes with particularly vulnerable populations and infrastructure.

Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast.

Anisotropic diffusion filters for flow-dependent variational data assimilation of sea surface temperature

It is well known that the background error covariance matrix in the data assimilation is anisotropic and varied with the flow field, that is, flow-dependent. However, it is generally assumed to be isotropic and static for ease of implementation. Therefore, to enhance the quality of the background error covariance, two easy-to-implement, flow-dependent three-dimensional variational analysis (3DVar) schemes, the advection–diffusion filter (ADF) and the Smagorinsky diffusion filter (SDF), are tentatively proposed. These serve as alternative schemes for the traditional methods that are based on the isotropic assumption. First, a correctness test of the gradient for the ADF is performed to verify the validity of the adjoint code. Then, the response analysis for the 1D advection–diffusion​ equation is investigated to validate the anisotropic characteristic of the ADF with various flow velocities. Second, the two flow-dependent 3DVar schemes are verified via single-observation assimilation experiments. Compared to the existing diffusion filter with a static diffusion coefficient, both new schemes show a flow-dependent capability in the assimilation experiment because they successfully spread the observational information anisotropically. Finally, a retroactive real-time forecast experiment framework based on a 3D baroclinic primitive equation ocean model is used to investigate the effects of the ADF/SDF on the analysis and forecast accuracy of sea surface temperature (SST). While the traditional variational assimilation method with an isotropic diffusion filter generally reduces initial field errors, both the ADF and SDF can further enhance the accuracy of the initial fields in strong current areas. In addition, better forecast skills can be obtained with the two flow-dependent schemes in the East China Sea.

Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation

Abstract. Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while PMW SSTs are valuable for constraining models during cloudy conditions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures on spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can easily be adapted for other observation variables.

Assessment of predictability of the Loop Current in the Gulf of Mexico from observing system experiments and observing system simulation experiments

This study presents results from numerical model experiments with a high-resolution regional forecast system to evaluate model predictability of the Loop Current (LC) system and assess the added value of different types of observations. The experiments evaluate the impact of surface versus subsurface observations as well as different combinations and spatial coverage of observations on the forecasts of the LC variability. The experiments use real observations (observing system experiments) and synthetic observations derived from a high-resolution independent simulation (observing system simulation experiments). Model predictability is assessed based on a saturated error growth model. The forecast error is computed for the sea surface height fields and the LC frontal positions derived from the forecasts and control fields using two metrics. Estimated model predictability of the LC ranges from 2 to 3 months. Predictability limit depends on activity state of the LC, with shorter predictability limit during active LC configurations. Assimilation of subsurface temperature and salinity profiles in the LC area have notable impact on the medium-range forecasts (2–3 months), whereas the impact is less prominent on shorter scales. The forecast error depends on the uncertainty of the initial state; therefore, on the accuracy of the analysis providing the initial fields. Forecasts with the smallest initial error have the best predictive skills with reliable predictability beyond 2 months suggesting that the impact of the model error is less prominent than the initial error. Hence, substantial improvements in forecasts up to 3 months can be achieved with increased accuracy of initialization.

Effects of shade and deficit irrigation on maize growth and development in fixed and dynamic AgriVoltaic systems

Maize production is essential for global food security and represents a major supply in several value chains. However, the projected effects of climate change are likely to decrease drastically water availability for crops in many regions, affecting yield. AgriVoltaics (AV) systems are an innovative solution that may improve maize resilience in water-scarce regions mainly by protecting plants from excessive radiation and by reducing irrigation needs. However, shade from panels may also affect crop development and production. This study addresses the interplay between radiation transmission, crop development and irrigation needs of maize cropping in field conditions, by the description of crop development dynamics, distinguishing between fixed and dynamic panels. We showed that maize crop responded to both independent and combined stresses (shade and water deficit), with a significant decrease in leaf area index, total dry matter and grain yield. Concerning water use, we showed the potential of AV to reduce irrigation inputs (by up to 19–47% compared to unshaded plots) via reduced soil water depletion and reference evapotranspiration. The crop development was impacted by shade by increasing phyllochron and causing a generalized delay in phenology. At a finer temporal scale, we concluded that maize leaves react to shade by reducing stomatal conductance, net assimilation of CO2 and leaf temperature in a correlated way to radiation, opening the possibility to use this behavior to optimize water use and shading strategies. The spatial heterogeneities of radiation in fixed AV systems, compared to dynamic AV systems, were identified as a second-order effect at the plot level on leaf area index and phyllochron, compared to the effect of radiation reduction. Moreover, dynamic AV showed their ability to reduce the spatial heterogeneities in soil water depletion, showing the importance of controlled shade strategies in AV systems concerning water use.

Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model

Sea surface temperature (SST) observations are a critical data set for long-term climate reconstruction. However, their assimilation with an ensemble-based data assimilation method can degrade performance in the ocean interior due to spurious covariances. Assimilation in isopycnal coordinates can delay the degradation, but it remains problematic for long reanalysis. We introduce vertical localization for SST assimilation in the isopycnal coordinate. The tapering functions are formulated empirically from a large pre-industrial ensemble. We propose three schemes: 1) a step function with a small localization radius that updates layers from the surface down to the first layer for which insignificant correlation with SST is found, 2) a step function with a large localization radius that updates layers down to the last layer for which significant correlation with SST is found, and 3) a flattop smooth tapering function. These tapering functions vary spatially and with the calendar month and are applied to isopycnal temperature and salinity. The impact of vertical localization on reanalysis performance is tested in identical twin experiments within the Norwegian Climate Prediction Model (NorCPM) with SST assimilation over the period 1980–2010. The SST assimilation without vertical localization greatly enhances performance in the whole water column but introduces a weak degradation at intermediate depths (e.g., 2,000–4,000 m). Vertical localization greatly reduces the degradation and improves the overall accuracy of the reanalysis, in particular in the North Pacific and the North Atlantic. A weak degradation remains in some regions below 2,000 m in the Southern Ocean. Among the three schemes, scheme 2) outperforms schemes 1) and 3) for temperature and salinity.

Land surface temperature assimilation into a soil moisture-temperature model for retrieving farm-scale root zone soil moisture

• Data are assimilated into soil moisture and temperature modules of HYDRUS-1D. • For SSM retrieval, assimilation of SSM data is superior to LST assimilation. • For retrieval of RZSM, LST assimilation is better than SSM assimilation. • Assimilation of LANDSAT8-LST data leads to a more accurate RZSM retrieval. • Assimilation of MODIS-LST results in more efficient estimation of soil parameters. Thermal infrared remote sensing have been extensively applied to estimate global- or regional- extent surface soil moisture. Meanwhile, potentials of the remotely sensed data for farm-scale retrieval of root zone soil moisture (RZSM) as well as estimation of soil hydraulic parameters, have been rarely investigated. Using Ensemble Kalman Filter, we propose a new methodology to assimilate land surface temperature (LST) of both MODIS and LANDSAT-8, into the soil temperature module of HYDRUS-1D model. The main objectives are to estimate soil hydraulic parameters and to retrieve RZSM with high spatiotemporal resolution independent of any in-situ measurements of soil temperature or moisture. However, we consider some modeling scenarios by which we assimilate in-situ measurements of soil moisture into the soil moisture module of the HYDRUS-1D model to provide a reference to compare with results of the LST assimilation scenarios. We apply the proposed methodology to a farm located in Moghan irrigation district, Ardabil province of Iran, which has in-situ soil moisture measurements. Even in the least accurate scenario of ours by which MODIS-LST was assimilated, RMSE varies in the range of 0.012–0.013 cm 3 ·cm −3 demonstrated to be superior compared to preceding recent works in the literature of satellite soil moisture retrieval. Moreover, the scenario of assimilating LANDSAT-LST data leads to higher parameter uncertainty compared to the assimilation of solely in-situ soil moisture or MODIS-LST which is related to higher temporal resolution of both in-situ and MODIS data compared to LANDSAT data and the error stems from the algorithm of deriving LANDSAT-LST. Accordingly, our study recommend that assimilation of the satellite-based land surface temperature of both LANDSAT-8 and MODIS are appropriate alternatives for expensive in-situ measurement.

Coupled data assimilation at ECMWF: current status, challenges and future developments

AbstractThis article presents coupled data assimilation (DA) activities at the European Centre for Medium‐Range Weather Forecasts (ECMWF). Coupled DA is an essential component of the ECMWF Earth‐system strategy. It aims at providing consistent initial conditions to the coupled atmosphere, land, and ocean forecast model. The article introduces the different DA systems and observing systems for each Earth‐system component. It discusses challenges related to observation consistency, availability, and sustainability across the components. It gives a review of coupling methodologies and presents coupling methods in development at the ECMWF. The current ECMWF system relies on weakly coupled DA approaches between land and atmosphere, between wave and atmosphere, and between ocean and atmosphere. Research on coupled DA has been centred on outer loop coupling developments and evaluation, focused on ocean–atmosphere coupling for reanalysis and for numerical weather prediction. The latest configuration of the ocean–atmosphere outer loop coupling combines weak and outer loop coupling methods. The article discusses the challenges in assimilating surface‐sensitive observations and it presents opportunities that coupled DA offers to enhance the exploitation of interface observations. It presents recent developments on land–atmosphere forward operator coupling and multilayer snow‐emission modelling and shows the potential for coupled radiative transfer modelling at low‐frequency passive microwave. The article discusses ongoing developments of in‐house sea‐surface temperature analysis based on coupled skin temperature assimilation. It presents preliminary results that demonstrate meaningful upper ocean temperature increments when assimilating skin temperature information from four‐dimensional variational assimilation. Coupled assimilation results are illustrated and discussed in the context of several applications related to reanalysis, scatterometer impact on coupled ocean–atmosphere, tropical cyclone, impact of weakly coupled sea‐ice–atmosphere data assimilation as well as land surface coupled DA case studies. These examples illustrate benefits and challenges of coupled DA developments. The article discusses future plans of coupled DA developments in support of ECMWF's Earth‐system 2021–2030 strategy.

The sensitivity of the WRF-4DVar data assimilation system to the control variables: A study on heavy rainfall events over India

The impact of different formulations of background error covariances (BECs) is examined for three heavy rainfall episodes over north India with a regional 4-dimensional variational (4DVar) data assimilation (DA) system. Three BEC formulations are analyzed, in which two of them employ stream function and velocity potential (ψ and χ) and the third one uses zonal and meridional velocity components (v and v) as momentum control variables. The uv-based formulation is completely univariate whereas, the correlations among the control variables are taken into account in the ψχ-based formulations through linear regression relations. Among the two ψχ-based BECs, one uses univariate relation and the other one uses multivariate relations for the moisture field. The multivariate relationship allows for impacting the moisture analysis through the assimilation of temperature or wind observations. Three experiments are carried out for each case with cyclic 4DVar assimilation. The conventional surface and upper-air observations are assimilated in combination with atmospheric motion vectors (AMVs) and ocean surface winds. Free forecast for 48 h is performed from respective final analysis fields for all the experiments. The results indicate that the uv-based analysis fields are closer to the observations. A comparative analysis of the 4DVar experiments with the 3DVar DA system provided a critical insight on the role of the 4DVar DA system on implicitly accounting for the multivariate correlations. The precipitation forecasts confirm the improved performance of the ψχ-based experiment, when multivariate nature of the humidity is incorporated. The time evolution of the intense rainfall episodes over the location of maximum rainfall are relatively well reproduced in the uv-based experiment. The results indicate that the inclusion of multivariate humidity variable in the BEC formulation does have a significant impact on suppressing the excessive overestimation in rainfall intensity.

Impacts of the Lagrangian Data Assimilation of Surface Drifters on Estimating Ocean Circulation during the Gulf of Mexico Grand Lagrangian Deployment

Abstract Satellite-tracked in situ surface drifters, providing measurements of near-surface ocean quantities, have become increasingly prevalent in the global ocean observation system. However, the position data from these instruments are typically not leveraged in operational ocean data assimilation (DA) systems. In this work, the impact of an augmented-state Lagrangian data assimilation (LaDA) method using the local ensemble Kalman transform filter is investigated within a realistic regional ocean DA system. Direct positioning data of surface drifters released by the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment during the summer 2012 Grand Lagrangian Deployment Experiment are assimilated using a Gulf of Mexico (GoM) configuration of the Modular Ocean Model, version 6, of the Geophysical Fluid Dynamics Laboratory. Multiple cases are tested using both 1/4° eddy-permitting and 1/12° eddy-resolving model resolutions: 1) a free running model simulation, 2) a conventional assimilation of temperature and salinity profile observations, 3) an assimilation of profiles and Lagrangian surface drifter positions, and 4) an assimilation of the profiles and derived Eulerian velocities. LaDA generally produces more accurate estimates of all fields compared to the assimilation of derived Eulerian velocities, with estimates of surface currents notably improving, when transitioning to 1/12° model resolution. In particular, LaDA produces the most accurate estimates of sea surface velocities under tropical cyclone conditions when Hurricane Isaac (2012) impacted the GoM. Further experiments applying a vertical localization while assimilating surface drifter positions improve the estimates of temperature and salinity below the mixed layer depth. Cases including the surface drifter positions in the DA show better Lagrangian predictability than the conventional DA.

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.

Stability analysis of functionals in variational data assimilation with respect to uncertainties of input data for a sea thermodynamics model

AbstractThe problem of stability and sensitivity of functionals of the optimal solution of the variational data assimilation of sea surface temperature for the model of sea thermodynamics is considered. The variational data assimilation problem is formulated as an optimal control problem to find the initial state and the boundary heat flux. The sensitivity of the response functions as functionals of the optimal solution with respect to the observation data is studied. Computing the gradient of the response function reduces to the solution of a non-standard problem being a coupled system of direct and adjoint equations with mutually dependent initial and boundary values. The algorithm to compute the gradient of the response function is presented, based on the Hessian of the original cost functional. Stability analysis of the response function with respect to uncertainties of input data is given. Numerical examples are presented for the Black and Azov seas thermodynamics model.