Temperature Assimilation Research Articles

Data analysis for variational assimilation of the surface temperature of the Black and Azov Seas

Data analysis for variational assimilation of the surface temperature of the Black and Azov Seas

NorCPM1 and its contribution to CMIP6 DCPP

Abstract. The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products. The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

Toward All-Sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System

Abstract The all-sky assimilation of radiances from microwave instruments is developed in the 4D-EnVar analysis system at Environment and Climate Change Canada (ECCC). Assimilation of cloud-affected radiances from Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels 4 and 5 for non-precipitating scenes over the ocean surface is the focus of this study. Cloud-affected radiances are discarded in the ECCC operational data assimilation system due to the limitations of forecast model physics, radiative transfer models, and the strong nonlinearity of the observation operator. In addition to using symmetric estimate of innovation standard deviation for quality control, a state-dependent observation error inflation is employed at the analysis stage. The background-state clouds are scaled by a factor of 0.5 to compensate for a systematic overestimation by the forecast model before being used in the observation operator. The changes in the fit of the background state to observations show mixed results. The number of AMSU-A channels 4 and 5 assimilated observations in the all-sky experiment is 5%–12% higher than in the operational system. The all-sky approach improves temperature analysis when verified against ECMWF operational analysis in the areas where the extra cloud-affected observations were assimilated. Statistically significant reductions in error standard deviation by 1%–4% for the analysis and forecasts of temperature, specific humidity, and horizontal wind speed up to maximum 4 days were achieved in the all-sky experiment in the lower troposphere. These improvements result mainly from the use of cloud information for computing the observation-minus-background departures. The operational implementation of all-sky assimilation is planned for the fall of 2021.

The Assimilation of Temperature and Salinity Profile Observations for Forecasting the River–Estuary–Shelf Waters

AbstractCurrent down‐scaling numerical modeling system around Hong Kong achieved a considerable prediction skill for the estuarine–shelf circulation off the Pearl River Estuary without data assimilation (DA). In order to further improve the reliability of this modeling system, the cost‐effective Ensemble optimal interpolation approach is implemented to test the potential benefits from assimilating the cruise temperature and salinity (T/S) profiles to reproduce the variable coast waters. Regarding assimilation parameters (e.g., assimilation window, observation spatiotemporal scales, and ensemble composition), four parallel experiments are conducted in summer 2015. Against the assimilated T/S profiles, the vertical structures of the analyzed T/S are improved by the DA, although the waters experience strong mixing on the shelf. Compared with the run without DA, the root mean square errors of the predicted T/S are generally reduced by 9.8%–23.5% and 4.2%–14.0% in the assimilation runs. The results also show the salinity stratification is improved in the shelf by the assimilation of T/S profiles, although the improvement is sensitive to the selected ensemble and the assimilation window. Further, we investigate the impact of the temporal scales of the river‐estuary‐shelf (RES) waters on the assimilation results by the sampling of the model‐state ensemble. The water exchanges between the estuary and the shelf are also better captured through this assimilation. The assimilation impact analysis shows that DA has advantages in reproducing the distribution of water masses of the RES waters, although the quality of the reproduced water mass distribution is related to the adopted sample ensemble in DA.

More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013

Texas experienced an extreme drought on record in 2011. Model simulations or satellite observations have been used to assess and analyze the drought. In this study, a method based on multi-source remote sensing data assimilation is proposed to evaluate the drought in Texas, which combines the advantages of model simulations and satellite observations. Brightness temperature from the Soil Moisture and Ocean Salinity (SMOS), the Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2) and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) are assimilated into the Noah-Multiparameterization model (Noah-MP) to improve the soil moisture and drought estimation. The Ensemble Kalman Filter (EnKF) was used as a data assimilation method with observation aggregation (dealing with the multi-resolution observations) and observation bias correction (dealing with the systematic bias between observations and simulations). Data from ground stations and satellites are used for the validation of soil moisture, terrestrial water storage anomalies, and evapotranspiration. With assimilation, slight improvement can be found for surface soil moisture over the open-loop configuration, but some notable degradations are also observed. More obvious improvement can be found at deep layers, which is mainly due to the assimilation of terrestrial water storage anomalies. In addition, the improvement for terrestrial water storage anomalies and evapotranspiration is more statistically significant in most regions of Texas. Overall, the assimilation experiment plays a positive role in the estimation of essential variables relate to drought. The drought is estimated using the soil moisture percentile and classified into five categories. The results from the assimilation experiment tend to predict a more severe drought in Texas for both magnitude and spatial patterns, which indicates not only the well-known drought in 2011 but also the continuous severe drought in 2012 and 2013. Moreover, the spatial distribution of drought reveals that drought occurred more frequently in the North, West, and Southwest.

Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations

AbstractData assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multi-source remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multi-source remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

Smoke-charged vortices in the stratosphere generated by wildfires and their behaviour in both hemispheres: comparing Australia 2020 to Canada 2017

Abstract. The two most intense wildfires of the last decade that took place in Canada in 2017 and Australia in 2019–2020 were followed by large injections of smoke into the stratosphere due to pyro-convection. After the Australian event, Khaykin et al. (2020) and Kablick et al. (2020) discovered that part of this smoke self-organized as anticyclonic confined vortices that rose in the mid-latitude stratosphere up to 35 km. Based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations and the ERA5 reanalysis, this new study analyses the Canadian case and finds, similarly, that a large plume had penetrated the stratosphere by 12–13 August 2017 and then became trapped within a mesoscale anticyclonic structure that travelled across the Atlantic. It then broke into three offspring that could be followed until mid-October, performing three round-the-world journeys and rising up to 23 km. We analyse the dynamical structure of the vortices produced by these two wildfires and demonstrate how the assimilation of the real temperature and ozone data from instruments measuring the signature of the vortices explains the appearance and maintenance of the vortices in the constructed dynamical fields. We propose that these vortices can be seen as bubbles of small, almost vanishing, potential vorticity and smoke carried vertically across the stratification from the troposphere inside the middle stratosphere by their internal heating, against the descending flux of the Brewer–Dobson circulation.

Impact of the Assimilation of INSAT-3D Sounder Retrieved Temperature and Humidity Profiles on Extreme Rainfall Event Forecast

In the present study, the impact of the assimilation of the Indian National Satellite (INSAT-3D) retrieved temperature and humidity profiles are evaluated during a heavy rainfall event. The Weather Research and Forecasting (WRF) and its three-dimensional variational (3D-Var) data assimilation system are used in this study. The extreme rainfall event which occurred during June 12th, 2017, to June 15th, 2017, over Bangladesh and adjoining region is taken as the case study. The analysis obtained after assimilation is compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) re-analysis. Results shows quite good improvement in temperature and humidity profiles when compared with ECMWF re-analysis. The model predicted 72 h rainfall forecast is also found to improve on the assimilation of INSAT-3D retrieved temperature and humidity profiles. From the present study, it is observed that INSAT-3D data have large impact on the humidity profiles after assimilation.

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Restoring the Boundary Condition and the Initial State in the Variational Data Assimilation Problem for the Black Sea Dynamics Model

In the present paper, we formulate an inverse problem of assimilation of sea surface temperature for a sea thermodynamics model aimed at the reconstruction of the heat flux and the initial state. We assume, that the unique function which is obtained by observation data processing is the function of sea surface temperature (SST). Numerical experiments on restoring the surface heat flux and the initial state for the system of the Black Sea primitive equation hydrodynamics model (temperature, salinity, velocity, and sea surface level) with assimilation procedure were carried. The results of the numerical experiments are presented.

Excessive productivity and heat content in tropical Pacific analyses: Disentangling the effects of in situ and altimetry assimilation

Monitoring and predicting global ocean biogeochemistry and marine ecosystems is one of the biggest challenges for the coming decade. In operational systems, biogeochemical (BGC) models are forced – or coupled – with physical ocean models fields that are generally constrained by data assimilation of temperature, salinity and sea level anomalies. Yet, while physical data assimilation substantially improves simulated physical fields, BGC models forced by such analyses are commonly degraded, and more especially in equatorial regions. Here impacts of physical data assimilation on surface chlorophyll and nitrate concentrations are investigated in the tropical Pacific, based on three ocean reanalysis runs using the same physical–BGC model configuration but differing in their level of physical data assimilation. It is shown that, in the Mercator Ocean operational system, the assimilation of satellite altimetry and sea surface temperature in addition to temperature and salinity in situ profiles leads to spurious vertical velocities in the western equatorial Pacific. Our analysis suggests that these unrealistic vertical velocities are explained by the use of an inaccurate mean dynamic topography for the assimilation of altimetry that modifies the pressure-driven horizontal circulation in the upper ocean layer. Moreover, the biases found in this key region modify the subtle dynamical and BGC balances in the whole tropical Pacific and result in unrealistic trends of ocean heat content and nitrate concentration. This study demonstrates that looking into details of the physics is indispensable to improve physical data assimilation systems and to ensure that they make the best use of observations. This is also a key point to refine the strategy of the BGC models forcing and further improve ocean predictions.

Data assimilation of sea surface temperature and salinity using basin-scale reconstruction from empirical orthogonal functions: a feasibility study in the northeastern Baltic Sea

Abstract. The tested data assimilation (DA) method based on EOF (Empirical Orthogonal Functions) reconstruction of observations decreased centred root-mean-square difference (RMSD) of surface temperature (SST) and salinity (SSS) in reference to observations in the NE Baltic Sea by 22 % and 34 %, respectively, compared to the control run without DA. The method is based on the covariance estimates from long-term model data. The amplitudes of the pre-calculated dominating EOF modes are estimated from point observations using least-squares optimization; the method builds the variables on a regular grid. The study used a large number of in situ FerryBox observations along four ship tracks from 1 May to 31 December 2015, and observations from research vessels. Within DA, observations were reconstructed as daily SST and SSS maps on the coarse grid with a resolution of 5 × 10 arcmin by N and E (ca. 5 nautical miles) and subsequently were interpolated to the fine grid of the prognostic model with a resolution of 0.5 × 1 arcmin by N and E (ca. 0.5 nautical miles). The fine-grid observational fields were used in the DA relaxation scheme with daily interval. DA with EOF reconstruction technique was found to be feasible for further implementation studies, since (1) the method that works on the large-scale patterns (mesoscale features are neglected by taking only the leading EOF modes) improves the high-resolution model performance by a comparable or even better degree than in the other published studies, and (2) the method is computationally effective.

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ~0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan

An ensemble Kalman filter is used to assimilate Advanced Microwave Scanning Radiometer-2 (AMSR2) observations of passive microwave (PMW) brightness temperatures (spectral differences, $\Delta T_b$ ) into land surface model estimates of snow mass over northwestern high mountain Asia (HMA). Trained support vector machines serve as the observation operator and map the geophysical modeled variables into $\Delta T_b$ space within the assimilation framework. Evaluation of the assimilation routine is carried out through comparison of assimilated snow mass estimates with an in situ dataset. The assimilation framework helps improve the land surface model estimates through PMW $\Delta T_b$ assimilation, particularly in terms of decreasing the domain-wide bias. The assimilation framework proved more effective during the (dry) snow accumulation season and decreased the bias and root-mean-square error (RMSE) in snow mass estimates at 76% and 58% of the comparative pixels, respectively. During the snow ablation season, the PMW brightness temperature signal contained less information related to snow mass due to the presence of other concurrent geophysical features that effectively serve as noise during the snow mass update. The utilization of PMW $\Delta T_b$ for accurate snow mass estimation in complex terrain such as HMA is dependent on a multitude of factors for optimal results; however, it does add utility to the land surface model if the relevant pitfalls are taken into consideration prior to the state variable update.

Interannual Variability of the Wind-Wave Regime Parameters in the Black Sea

Purpose. The investigation is aimed at increasing accuracy of the temperature field reconstruction in the Black Sea upper layer. For this purpose, satellite observations of the sea surface temperature and the three-dimensional fields of temperature (in the 50–500 m layer) and salinity (in the 2.5–500 m layer) pseudo-measurements, previously calculated by the altimetry and the Argo floats data, were jointly assimilated in the Marine Hydrophysical Institute model. Methods and Results. Assimilation of the sea surface temperature satellite observations is the most effective instrument in case the discrepancies between the sea surface and the model temperatures are extrapolated over the upper mixed layer depth up to its lower boundary. Having been analyzed, the temperature profiles resulted from the forecast calculation for 2012 and from the Argo float measurements made it possible to obtain a simple criterion (bound to the model grid) for determining the upper mixed layer depth, namely the horizon on which the temperature gradient was less or equal to ≤ 0.017 °C/m. Within the upper mixed layer depth, the nudging procedure of satellite temperature measurements with the selected relaxation factor and the measurement errors taken into account was used in the heat transfer equation. The temperature and salinity pseudo-measurements were assimilated in the model by the previously proposed adaptive statistics method. To test the results of the sea surface temperature assimilation, the Black Sea hydrophysical fields were reanalyzed for 2012. The winter-spring period (January – April, December) is characterized by the high upper mixed layer depths, well reproducible by the Pacanowski – Philander parameterization, and also by the low values (as compared to the measured ones) of the basin-averaged monthly mean square deviations of the simulated temperature fields. The increased mean square deviations in July – September are explained by absence of the upper mixed layer in the temperature profiles measured by the Argo floats that is not reproduced by the Pacanowski – Philander parameterization. Conclusions. The algorithm for assimilating the sea surface temperature together with the profiles of the temperature and salinity pseudo-measurements reconstructed from the altimetry data was realized. Application of the upper mixed layer depths estimated by the temperature vertical profiles made it possible to correct effectively the model temperature by the satellite-derived sea surface temperature, especially for a winter-spring period. It permitted to reconstruct the temperature fields in the sea upper layer for 2012 with acceptable accuracy.

Soil Moisture–Evapotranspiration Overcoupling and L-Band Brightness Temperature Assimilation: Sources and Forecast Implications

AbstractThe assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.

Vosstanovleniye gidrofizicheskikh poley Chernogo morya s assimilyatsiyey poverkhnostnoy temperatury i psevdoizmereniy temperatury, solenosti v modeli

Purpose. The investigation is aimed at increasing accuracy of the temperature field reconstruction in the Black Sea upper layer. For this purpose, satellite observations of the sea surface temperature and the three-dimensional fields of temperature (in the 50–500 m layer) and salinity (in the 2.5–500 m layer) pseudo-measurements, previously calculated by the altimetry and the Argo floats data, were jointly assimilated in the Marine Hydrophysical Institute model. Methods and Results. Assimilation of the sea surface temperature satellite observations is the most effective instrument in case the discrepancies between the sea surface and the model temperatures are extrapolated over the upper mixed layer depth up to its lower boundary. Having been analyzed, the temperature profiles resulted from the forecast calculation for 2012 and from the Argo float measurements made it possible to obtain a simple criterion (bound to the model grid) for determining the upper mixed layer depth, namely the horizon on which the temperature gradient was less or equal to 0.017°C/m. Within the upper mixed layer depth, the nudging procedure of satellite temperature measurements with the selected relaxation factor and the measurement errors taken into account was used in the heat transfer equation. The temperature and salinity pseudo-measurements were assimilated in the model by the previously proposed adaptive statistics method. To test the results of the sea surface temperature assimilation, the Black Sea hydrophysical fields were reanalyzed for 2012. The winterspring period (January – April, December) is characterized by the high upper mixed layer depths, well reproducible by the Pacanowsci – Philander parameterization, and also by the low values (as compared to the measured ones) of the basin-averaged monthly mean square deviations of the simulated temperature fields. The increased mean square deviations in July – September are explained by absence of the upper mixed layer in the temperature profiles measured by the Argo floats that is not reproduced by the Pacanowsci – Philander parameterization. Conclusions. The algorithm for assimilating the sea surface temperature together with the profiles of the temperature and salinity pseudo-measurements reconstructed from the altimetry data was realized. Application of the upper mixed layer depths estimated by the temperature vertical profiles made it possible to correct effectively the model temperature by the satellite-derived sea surface temperature, especially for a winter-spring period. It permitted to reconstruct the temperature fields in the sea upper layer for 2012 with acceptable accuracy.

Variational Data Assimilation in Problems of Modeling Hydrophysical Fields in Open Water Areas

The formulation of boundary conditions at liquid (open) boundaries is a topical problem in mathematically modeling the hydrothermodynamics of open water areas. Variational data assimilation is one method allowing one to take into account liquid boundaries in models. According to the approach considered in this paper, observational data at a certain time are given and the problem is treated as an inverse one with open boundary flows as additional unknowns. This paper presents a formulation of the general problem of the variational assimilation of observational data for a model of the hydrothermodynamics of open water areas based on the splitting method. Algorithms for the variational assimilation of temperature and sea-level data at the liquid boundary are formulated and the results of numerical experiments on the use of the algorithms in the Baltic Sea circulation model are presented.

Variational Assimilation of the Impervious Surfaces Temperature

Impervious surface temperature is a key parameter in impervious surface energy balance. Urban canopy models are now widely used to simulate impervious surface temperatures, but the physical assimilation method for the urban canopy model is still under development and the use of high temporal resolution observation data are limited. In this paper, a physical assimilation method was used to improve the simulation of the impervious surface temperature for the first time. A variational assimilation method was developed and coupled with the integrated urban land model, using the impervious surface energy balance equation as the adjoint physical constraint. The results showed that when the observed impervious surface temperature data of every timestep were assimilated into the integrated urban land model, the bias of the impervious surface temperature was reduced about 86 %. For the operational run, the observed data were assimilated twice per day, and the bias of the impervious surface temperature was reduced by about 78%.