Data Assimilation

For a data series with grid points that do not uniformly cover a whole wave cycle in both space and time, traditional least squares fitting methods will lead to biased estimates of the zonal mean and wave components as a result of aliasing between these elements. We introduce a set of formal regularity conditions to select available grid points that reduce aliasing while obtaining the zonal mean and tidal components from unevenly sampled or incomplete measurements. The algorithm is used to self‐consistently investigate the effect of the zonal mean field on the seasonal variation of diurnal tide in the upper middle atmosphere using temperature measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics (TIMED) satellite. We first unbiasly derive both the zonal mean and migrating tidal components from the measurements, then simulate the seasonal variation of the migrating diurn...

Within the framework of the European MFSTEP project, an advanced multivariate sequential data assimilation system has been implemented to assimilate real chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) into a threedimensional biochemical model of the Eastern Mediterranean. The physical ocean is described through the Princeton Ocean Model (POM) while the biochemistry of the ecosystem is tackled with the Biochemical Flux Model (BFM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics were parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). A radius of influence was further selected around every data point to limit the range of the EOFs spatial correlations. The assimilation experiment was performed for one year over 1999 and forced with ECMWF 6 hour atmospheric fields. The accuracy of the ecological state identification by the assimilation system is assessed by the relevance of the system in fitting the data, and through the impact of the assimilation on non-observed biochemical processes. Assimilation of SeaWiFS data significantly improves the forecasting capability of the BFM model. Results, however, indicate the necessity of subsurface data to enhance the controllability of the ecosystem model in the deep layers.

The authors have developed an assimilation system toward coastal data assimilation around Japan, which consists of a four-dimensional variational (4DVAR) assimilation scheme with an eddy-resolving model in the western North Pacific (MOVE-4DVAR-WNP) and a fine-resolution coastal model covering the western part of the Japanese coastal region around the Seto Inland Sea (MOVE-Seto). The 4DVAR scheme is developed as a natural extension of the 3DVAR scheme used in the Meteorological Research Institute Multivariate Ocean Variational Estimation (MOVE) system. An initialization scheme of incremental analysis update (IAU) is incorporated into MOVE-4DVAR-WNP to filter out high-frequency noises. During the backward integration of the adjoint model, it works as an incremental digital filtering. MOVE-Seto, which is nested within MOVE-4DVAR-WNP, also employs IAU to initialize the interior of the coastal model using MOVE-4DVAR-WNP analysis fields. The authors conducted an assimilation experiment using MOVE-4DVAR-WNP, and results were compared with an additional experiment using the 3DVAR scheme. The comparison reveals that MOVE-4DVAR-WNP improves mesoscale variability. In particular, short-term variability such as small-scale Kuroshio fluctuations is much enhanced. Using MOVE-Seto and MOVE-4DVAR-WNP, the authors also performed a case study focused on an unusual tide event that occurred at the south coast of Japan in September 2011. MOVE-Seto succeeds in reproducing a significant sea level rise associated with this event, indicating the effectiveness of the newly developed system for coastal sea level variability.

The GODAE OceanView systems use various data assimilation algorithms, including 3DVar, EnOI, EnKF and the SEEK filter with a fixed basis, using different time windows. The main outputs of the operational data assimilation systems, the increments, have been compared for February 2014 in various regions. The eddy-permitting systems’ increments are similar in a number of the regions, indicating similar forecast errors are being corrected, while the eddy-resolving systems represent smaller-scale structures in the mid-latitude regions investigated and appear to have smaller biases. Monthly average temperature increments show significant SST biases, particularly in the systems which assimilate swath satellite SST data, indicating systematic errors in the surface heat fluxes and the way in which they are propagated vertically by the ocean models. On-going developments to the data assimilation systems include improvements to the specification of error covariances, improving assimilation of ...

What are main drivers?  Ocean DA systems are an essential component of general S-I forecasting systems in operational centers as well as Coupled ocean-atmosphere General Circulation Models (CGCMs)  The estimation of the tropical Pacific Ocean state through ocean DA is vital for S-I forecasting system because most predictability for S-I forecasts comes from ENSO.  Ocean DA systems are also employed in operational centers for monitoring the equatorial wave activity, variability of the thermocline along the equator, and other ocean phenomena associated with ENSO. Typical specification of current operational ocean DA systems for S-I forecasts  Resolution: 0.5-1˚ in horizontal (no eddy model), 10 m in vertical  It is desirable to be used in ensemble forecasts as a part of CGCMs. It needs to be light for computing.  But UKMO started to use a 1/4˚ resolution ocean model, recently.  Assimilation scheme: 3DVAR (NCEP, JMA, ECMWF), EnOI (ABoM)  NCEP uses a "weakly coupled" data assimilation scheme.

Abstract. The global ocean is a significant sink for anthropogenic carbon (Cant), absorbing roughly a third of human CO2 emitted over the industrial period. Robust estimates of the magnitude and variability of the storage and distribution of Cant in the ocean are therefore important for understanding the human impact on climate. In this synthesis we review observational and model-based estimates of the storage and transport of Cant in the ocean. We pay particular attention to the uncertainties and potential biases inherent in different inference schemes. On a global scale, three data-based estimates of the distribution and inventory of Cant are now available. While the inventories are found to agree within their uncertainty, there are considerable differences in the spatial distribution. We also present a review of the progress made in the application of inverse and data assimilation techniques which combine ocean interior estimates of Cant with numerical ocean circulation models. S...

Introduction: Pakistan’s heterogeneous climate exposes its vegetation to alternating drought, heat, and flood episodes, yet province-scale responses remain poorly resolved.Methods: This study assessed climate-driven vegetation vulnerability across Pakistan from 2001 to 2023 by combining Landsat-based kernel NDVI (kNDVI), the Vegetation Health Index (VHI), Mann–Kendall trend analysis, and interpretable machine learning. Annual vegetation dynamics were evaluated across Balochistan (BLC), Khyber Pakhtunkhwa–Gilgit–Baltistan–AJK (KGA), Punjab (PJB), and Sindh (SND), and related to precipitation, temperature, SPEI, and PDSI. Random Forest with SHAP was used to quantify the relative contribution of climatic predictors to kNDVI.Results: The record showed a persistent spatial contrast: BLC and southern SND remained the most moisture- and heat-constrained, with up to 77% and 59% of their areas, respectively, falling into decline classes, whereas PJB retained the most stable vegetation, supported by irrigation and stronger monsoon recharge. kNDVI captured cumulative stresses in BLC and KGA, where negative trends persisted despite wetter years, while VHI registered rapid post-flood greening after the 2010 and 2022 events, particularly in SND, reflecting short-term improvement in vegetation condition rather than sustained recovery. SHAP analysis identified moisture-related variables (precipitation, PDSI, and SPEI) as the dominant positive controls in KGA and PJB, whereas high temperature exerted the strongest negative influence in BLC and SND.Discussion: The dual-index framework distinguishes areas where vegetation improvement is consistent with better hydroclimatic conditions from areas where decline persists despite climatic relief, providing a basis for drought-resilient afforestation, rangeland rehabilitation, and climate-informed land-use planning in Pakistan.

Irrigation water use (IWU) or withdrawal is a key component for the water management of a region since it tends to exceed the crops consumptive water use, especially in water-stressed regions where groundwater is the main source of water. Nevertheless, temporal IWU information is missing in many irrigation areas. Remote sensing (RS) data is commonly used for crop water requirements estimations in areas with lack of data, however, IWU is more complex to approach since it also depends on water use efficiency, irrigation system type, irrigation scheduling, and water availability, among others. This work explores the use of remote sensing data (TRMM, MODIS) and land surface hydrological products (GLDAS 2 and MERRA 2) to obtain insights about the space-time annual IWU patterns across croplands located within Mexico’s northeast region. Reported IWU in three irrigation districts (Don Martín, Región Lagunera and Bajo Río Bravo) was used to obtain a functional model using satellite data d...

NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil ...

Empirical models of the ionosphere, such as the International Reference Ionosphere (IRI) model, play a vital role in evaluating the environmental effect on the operation of space-based communication and navigation technologies. The IRI extended to Plasmasphere (IRI-Plas) model can be adjusted with external data to update its electron density profile while still maintaining the overall integrity of the model representations. In this paper, the performance of the total electron content (TEC) assimilation option of the IRI-Plas at two equatorial stations, Jicamarca, Peru (geographic: 12°S, 77°W, dip angle 0.8°) and Cachoeira Paulista, Brazil (Geographic: 22.7°S, 45°W, dip angle À26°), is examined during quiet and disturbed conditions. TEC, F 2 layer critical frequency (f o F 2 ), and peak height (h m F 2 ) predicted when the model is operated without external input were used as a baseline in our model evaluation. Results indicate that TEC predicted by the assimilation option generally produced smaller estimation errors compared to the "no extra input" option during quiet and disturbed conditions. Generally, the error is smaller at the equatorial trough than near the crest for both quiet and disturbed days. With assimilation option, there is a substantial improvement of storm time estimations when compared with quiet time predictions. The improvement is, however, independent on storm's severity. Furthermore, the modeled f o F 2 and h m F 2 are generally poor with TEC assimilation, particularly the h m F 2 prediction, at the two locations during both quiet and disturbed conditions. Consequently, IRI-Plas model assimilated with TEC value only may not be sufficient where more realistic instantaneous values of peak parameters are required.

Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. Data assimilation is the discipline that studies the combination of mathematical models and observations. It can improve the accuracy of mathematical models by incorporating data, but also brings challenges by increasing complexity and computational cost. In this paper, we study the application of data assimilation methods to the coupled dual porosity Stokes model. We give a brief introduction to the coupled model and examine the performance of different data assimilation methods on a finite element implementation of the coupled dual porosity Stokes system. We also study how observations on different variables of the system affect the data assimilation process.

This study provided an extension to the latest version of Lamont-Doherty Earth Observation (LDEO5) prediction system. First, an ensemble coupled data assimilation (CDA) system, based on the Ensemble Kalman Filter, was established. Both the Kaplan sea surface temperature (SST) data from January 1856 to December 2018 and the ECMWF twentieth century reanalysis (ERA-20C) wind data from January 1900 to February 2010 were assimilated for prediction initialization. Second, an ensemble prediction (EP) system was established using stochastic optimal perturbation that represented the uncertainty in the physical process. The assimilation experiments showed that assimilating multi-source data yielded better results than assimilating single-source data. The analyses of Niño3.4 SST anomalies and zonal wind stress (ZWS) anomalies were in good agreement with the observed counterparts, respectively. The root mean square errors of both Niño3.4 SST anomalies and ZWS anomalies were found to be significantly reduced, compared to the values obtained before assimilation. The modeled upper layer depth anomalies along the equator, and subsurface temperature anomalies in the Niño3.4 region were also found to be similar to the observed counterparts. A long-term ensemble hindcast was conducted using the EP system for the past 163 years, from January 1856 to December 2018. Results showed that the predictions initialized by assimilating multisource data yielded best deterministic skill, reaching the international advanced level. A comparative analysis revealed that the EP system predicted the warm events well, followed by cold and neutral events.

In this study, the assimilation of historic SST (sea surface temperature) data was performed for long-term ENSO hindcasts. The emphasis was placed on the design of background error covariance (BEC) that dominates the transfer of SST information to the subsurface. Four different data-assimilation schemes, based on Optimal Interpolation (OI) algorithm, were proposed, and compared in terms of ENSO simulation and prediction skills for the period from 1876 to 2000. It was found that the data-assimilation scheme that has a three-dimensional BEC constructed from model simulations forced by observed wind stress can effectively correct the second-layer temperature in the SST assimilation and lead to the best ENSO prediction skill. Further analysis for the long-term hindcasts shows that the prediction skills have a striking decadal/interdecadal variability similar to that found in other models. These results provide a fundamental basis for the further study of ENSO predictability.

Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower trophic level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20 % random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellite-derived data does not pose a significant problem to the assimilation of satellite data into these models. On the contrary, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, highlighting the potential danger of overtuning inherent in such complex models.

Now that regional circulation patterns can be reasonably well reproduced by ocean circulation models, significant effort is being directed toward incorporating complex food webs into these models, many of which now routinely include multiple phytoplankton (P) and zooplankton (Z) compartments. This study quantitatively assesses how the number of phytoplankton and zooplankton compartments affects the ability of a lower-trophic-level ecosystem model to reproduce and predict observed patterns in surface chlorophyll and particulate organic carbon. Five ecosystem model variants are implemented in a one-dimensional assimilative (variational adjoint) model testbed in the Mid-Atlantic Bight. The five models are identical except for variations in the level of complexity included in the lower trophic levels, which range from a simple 1P1Z food web to a considerably more complex 3P2Z food web. The five models assimilated satellite-derived chlorophyll and particulate organic carbon concentrations at four continental shelf sites, and the resulting optimal parameters were tested at five independent sites in a cross-validation experiment. Although all five models showed improvements in model-data misfits after assimilation, overall the moderately complex 2P2Z model was associated with the highest model skill. Additional experiments were conducted in which 20 % random noise was added to the satellite data prior to assimilation. The 1P and 2P models successfully reproduced nearly identical optimal parameters regardless of whether or not noise was added to the assimilated data, suggesting that random noise inherent in satellitederived data does not pose a significant problem to the assimilation of satellite data into these models. However, the most complex model tested (3P2Z) was sensitive to the level of random noise added to the data prior to assimilation, high-lighting the potential danger of over-tuning inherent in such complex models.

An adaptive mesh adjoint model has been developed to invert for uncertain parameters in the simulation of costal flows. These may include, for example, open boundary conditions and initial conditions. Our objective in this paper is to obtain a better representation of water flows around and over coastal topography. In this paper a first attempt has been made at using mesh adaptivity in both transient forward and adjoint models. Although the resulting method has inconsistent discrete schemes for the forward and adjoint models, the inversion results shown here are nevertheless good. The derivation of the continuous adjoint model is presented. A description of the adaptive mesh adjoint technique is also given. As a study case, the method is applied to the inversion of boundary conditions for flow past a headland.

The National Weather Service (NWS) is the agency responsible for flood forecasting in the U.S. NWS primarily uses the SNOW17 model for operational forecasting of snowmelt, which then serves as an input to rainfall-runoff models for streamflow forecasts in snow-affected areas. To improve snowmelt estimates and therefore streamflow forecasts, a principle component regression (PCR) technique is typically adopted to produce areal Snow Water Equivalent (SWE) data from point SWE measurements (e.g., from SNOTEL sites) in operations. While this method is parsimonious and easy to use in operations, it does not capitalize on the full capabilities offered by advanced data assimilation techniques to quantify, reduce, and propagate forecast uncertainty in a statistically and dynamically consistent fashion. This study investigates assimilating point SNOTEL SWE observations and PCRbased areal SWE estimates into the SNOW17, via direct insertion and an ensemble Kalman filter (EnKF). The performance ...

Networked computer clusters present the opportunity for low-cost high-perfor-
mance computing and call for new programming paradigms to effectively exploit
the distributed computational power. This paper describes the Brokered Objects
for Ragged-network Gigaflops (BORG) developed at the Applied Physics Labo-
ratory of the Johns Hopkins University. The BORG programming system allows
its users to program in the logical domain of the problems without concern of the
underlying computer network. Data dependencies are automatically translated
by the system into CORBA object invocations as needed. The CORBA-based
approach makes it easy for a BORG system to be configured dynamically with
components running on different platforms, operation system, and programming
languages. It supports programming under the model of divide-and-conquer with
distributed array objects that can be recursively divided and combined. Applica-
tions for radar signal propagation, information retrieval, and space-craft simulation
are being developed on BORG.
1 Introduction
Brokered Objects for Ragged-network Gigaflops (BOR

An idealized fluid model of convective-scale numerical weather prediction, intended for use in inexpensive data assimilation experiments, is described here and its distinctive dynamics are investigated. The model modifies the rotating shallow water equations to include some simplified dynamics of cumulus convection and associated precipitation, extending and improving the model of Würsch and Craig. Changes to this original model are the removal of ad hoc diffusive terms and the addition of Coriolis rotation terms, leading to a so-called 1.5-dimensional model. Despite the non-trivial modifications to the parent equations, it is shown that this shallow water type model remains hyperbolic in character and can be integrated accordingly using a discontinuous Galerkin finite element method for nonconservative hyperbolic systems of partial differential equations. Combined with methods to ensure well-balancedness and nonnegativity, the resulting numerical solver is novel, efficient and robust. Classical numerical experiments in the shallow water theory, such as the Rossby geostrophic adjustment and flow over topography, are reproduced for the standard shallow water model and used to highlight the modified dynamics of the new model. In particular, it exhibits important aspects of convective-scale dynamics relating to the disruption of large-scale balance and is able to simulate other features related to convecting and precipitating weather systems. Our analysis here and preliminary results suggest that the model is well suited for efficiently and robustly investigating data assimilation schemes in an idealized 'convectivescale' forecast assimilation framework.

We present a comprehensive comparison of polar processing diagnostics derived from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective-analysis for Research and Applications (MERRA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim). We use diagnostics that focus on meteorological conditions related to stratospheric chemical ozone loss based on temperatures, polar vortex dynamics, and air parcel trajectories to evaluate the effects these reanalyses might have on polar processing studies. Our results show that the agreement between MERRA and ERA-Interim changes significantly over the 34 years from 1979 to 2013 in both hemispheres and in many cases improves. By comparing our diagnostics during five time periods when an increasing number of higher-quality observations were brought into these reanalyses, we show how changes in the data assimilation systems (DAS) of MERRA and ERA-Interim affected their meteorological data. Many of our stratospheric temperature diagnostics show a convergence toward significantly better agreement, in both hemispheres, after 2001 when Aqua and GOES (Geostationary Operational Environmental Satellite) radiances were introduced into the DAS. Other diagnostics, such as the winter mean volume of air with temperatures below polar stratospheric cloud formation thresholds (V PSC ) and some diagnostics of polar vortex size and strength, do not show improved agreement between the two reanalyses in recent years when data inputs into the DAS were more comprehensive. The polar processing diagnostics calculated from MERRA and ERA-Interim agree much better than those calculated from earlier reanalysis data sets. We still, however, see fairly large differences in many of the diagnostics in years prior to 2002, raising the possibility that the choice of one reanalysis over another could significantly influence the results of polar processing studies. After 2002, we see overall good agreement among the diagnostics, which demonstrates that the ERA-Interim and MERRA reanalyses are equally appropriate choices for polar processing studies of recent Arctic and Antarctic winters.

The relative entropy is compared with the previously used Shannon entropy difference as a measure of the amount of information extracted from observations by an optimal analysis in terms of the changes in the probability density function (pdf) produced by the analysis with respect to the background pdf. It is shown that the relative entropy measures both the signal and dispersion parts of the information content from observations, while the Shannon entropy difference measures only the dispersion part. When the pdfs are Gaussian or transformed to Gaussian, the signal part of the information content is given by a weighted inner-product of the analysis increment vector and the dispersion part is given by a non-negative definite function of the analysis and background covariance matrices. When the observation space is transformed based on the singular value decomposition of the scaled observation operator, the information content becomes separable between components associated with different singular values. Densely distributed observations can be then compressed with minimum information loss by truncating the components associate with the smallest singular values. The differences between the relative entropy and Shannon entropy difference in measuring information content and information loss are analysed in details and illustrated by examples.

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...

The satellite era brings new challenges in the development and the implementation of potential field models. Major aspects are, therefore, the exploitation of existing space- and ground-based gravity and magnetic data for the long-term. Moreover, a continuous and near real-time global monitoring of the Earth system, allows for a consistent integration and assimilation of these data into complex models of the Earth’s gravity and magnetic fields, which have to consider the constantly increasing amount of available data. In this paper we propose how to speed up the computation of the normal equation in potential filed modeling by using local multi-polar approximations of the modeling functions. The basic idea is to take advantage of the rather smooth behavior of the internal fields at the satellite altitude and to replace the full available gravity or magnetic data by a collection of local moments. We also investigate what are the optimal values for the free parameters of our method. R...

The Observing System Research and Predictability Experiment (THORPEX) was a 10-yr, international research program organized by the World Meteorological Organization’s World Weather Research Program. THORPEX was motivated by the need to accelerate the rate of improvement in the accuracy of 1-day to 2-week forecasts of high-impact weather for the benefit of society, the economy, and the environment. THORPEX, which took place from 2005 to 2014, was the first major international program focusing on the advancement of global numerical weather prediction systems since the Global Atmospheric Research Program, which took place almost 40 years earlier, from 1967 through 1982. The scientific achievements of THORPEX were accomplished through bringing together scientists from operational centers, research laboratories, and the academic community to collaborate on research that would ultimately advance operational predictive skill. THORPEX included an unprecedented effort to make operational pro...

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer ov...

GLORYS12 is a global eddy-resolving physical ocean and sea ice reanalysis at 1/12° horizontal resolution covering the 1993-present altimetry period, designed and implemented in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). The model component is the NEMO platform driven at the surface by atmospheric conditions from the ECMWF ERA-Interim reanalysis. Ocean observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter sea level anomaly, satellite sea surface temperature and sea ice concentration, as well as in situ temperature and salinity vertical profiles are jointly assimilated. A 3D-VAR scheme provides an additional correction for the slowly-evolving large-scale biases in temperature and salinity. The performance of the reanalysis shows a clear dependency on the time-dependent in situ observation system. The general assessment of GLORYS12 highlights a level of performance at the state-of-the-art and the capacity of th...

Using observations the model skill can be quantified by a suitable predefined scalar function /, giving a measure of distance between observed and modelled quantities. Minimising this cost function by variation of a selected parameter set leads to an improvement of model skill, provided that the observations are representative. Potential candidates for optimisation parameters are e.g. initial and boundary values of the species, emission rates, and deposition velocities. In the present study the initial values of the model are taken as control (optimisation) parameters. The method applied is the four dimensional variational data assimilation (4D-var) which iteratively minimises the misfit of modelled concentration levels with observations. The gradient of the cost function is calculated by the adjoint model. In our experiments we use the University of Cologne EURopean Air pollution Dispersion model (EURAD) simulating on the mesoscale-a. The forward and adjoint components are Bott's (1989) horizontal and vertical advection scheme, implicit vertical diffusion, and the RADM2 gas phase chemistry . One of the problems in adjoint modelling is to cope with storage limitations. Our approach to this is presented in Fig. .

Application of tropospheric chemical transport models (CTMs) to chemical weather forecast on the global and regional scale is growing. In this paper advances and problems of numerical forecasts with Eulerian regional models are treated. After reviewing the progress of computer performance since the appearance of first complex CTMs, selected models are briefly introduced in order to demonstrate the spectrum of forecast activities. Ozone and particulate matter are chosen as examples of air quality forecasts on continental (European) and smaller spatial scales. The question of forecast and model evaluation is discussed and the potential of chemical data assimilation for the upcoming fourth generation of air quality forecast models emphasized.

Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in ...

Abstract. This paper presents the development and implementation of a spatio-temporal variational data assimilation system (4D-var) for the soil–vegetation–atmosphere transfer model "Community Land Model" (CLM3.5), along with the development of the adjoint code for the core soil–atmosphere transfer scheme of energy and soil moisture. The purpose of this work is to obtain an improved estimation technique for the energy fluxes (sensible and latent heat fluxes) between the soil and the atmosphere. Optimal assessments of these fluxes are neither available from model simulations nor measurements alone, while a 4D-var data assimilation has the potential to combine both information sources by a Best Linear Unbiased Estimate (BLUE). The 4D-var method requires the development of the adjoint model of the CLM which is established in this work. The new data assimilation algorithm is able to assimilate soil temperature and soil moisture measurements for one-dimensional columns of the model grid....

A tangent linear version of the complex soil–vegetation–atmosphere transfer model Community Land Model has been developed and its ability to reproduce relevant sensitivities of the modeled soil moisture and latent heat flux (LE) evolution to a number of different parameters on a short time scale (3 d) was analyzed. To this end, a series of idealized experiments for different soil moisture states and three different soil types was conducted. We found that the tangent linear model performs well for a large range of conditions. Situations in which the linear approximation potentially fails were connected with the occurrence of saturation and with the highly nonlinear parameterization of water availability to plants. The sensitivity of LE with respect to the model's initial soil moisture state was calculated and compared with the LE sensitivity to soil texture, leaf area index (LAI), and vegetation roughness length. These sensitivities were found to be highly variable with soil type...

The National Center for Atmospheric Research (NCAR) Regional Climate Model (RegCM2) is employed to investigate the physical mechanisms that govern the October-December rains over eastern Africa. The model employs the Mercator conformal projection, with a domain of 5580 km x 5040 km centered at 31øE, 4øS, and a horizontal grid point spacing of 60 km. The simulation period is October-December 1988, and the model initial and lateral boundary conditions are taken from ECMWF reanalysis. A number of month-long simulations have been conducted to optimize various parameterizations of the model which include the following factors: cumulus convection, moisture parameterization, radiative transfer formulation, surface processes, boundary layer physics, and the lateral boundary conditions. The model was successfully customized over eastern Africa. The model simulates the large-scale circulation characteristics over the region as well as local features such as the dominant precipitation maxima, the Turkana low-level jet, and the diurnal reversal in the lake/land breeze circulation over Lake Victoria. Several model deficiencies are also identified. They include a negative rainfall bias over the western portions of the domain and the Kenya Highlands and a temperature bias over the tropical forest regions. Systematic analysis of surface water budget reveals that evapotranspiration is a major sink in the water budget over the regions where precipitation is moderate or small, while the role of runoff and drainage becomes important over the regions where precipitation is abundant. The model simulations also suggest that during the short-rains season, the large-scale circulation anomalies play the most important role in shaping the precipitation anomalies. 1. 6530 SUN ET AL.: SIMULATION OF EASTERN AFRICA SHORT RAINS, i as affected by the lateral boundary large-scale forcing and the internal high-resolution forcing characteristic of the region's morphology. The southeast and northeast monsoons prevail over East Africa during the high-and low-insolation seasons, respectively. Both monsoons are thermally stable and associated with subsiding air, and most of East Africa experiences rainfall peaks during the monsoon transition periods: March through May and October through December. We will focus our investigation on the period of the short rains during which precipitation presents complex temporal and spatial characteristics that affect agriculture and water use in eastern African countries. In this study we investigate the performance of the NCAR RegCM in reproducing circulation, precipitation, temperature, and water vapor patterns for October-December 1988, since this represents near-average climatic conditions over East Africa, as shown by Ininda [1994]. The ECMWF (European Center for Medium-Range Weather Forecasts) reanalysis, which is used to generate the initial and lateral boundary conditions for the RegCM runs, and a limited amount of available station observations are used to evaluate the model simulations. Particular focus is placed on the onset of the short-rains season, lake effects, and the Turkana low-level jet, which GCMs generally fail to reproduce. An analysis of land-surface processes is also presented, as this is one of the main interests in the application of the model to seasonal climate prediction. We explore the ability of the NCAR RegCM to reproduce interannual climate variability in a companion paper. The paper is organized as follows: Sections 2 and 3 describe the model characteristics and the experimental design, respectively. Results from our model customization for eastern Africa are presented in section 4. These results are discussed in some detail in order to provide an illustrative example for other users of the RegCM. The full simulation of the short rains of 1988 is then discussed in section 5, and the main conclusions and the implications of our findings are discussed in section 6. The model used in this study is the NCAR secondgeneration regional climate model (hereinafter referred to as NCAR RegCM2), which is described in detail by Giorgi ½t al. [1993a, b]. The RegCM2 is based on the NCAR/Penn State mesoscale model version MM4 [Anthes et al., 1987]. It is a primitive equation, grid point limited area model. The equations satisfy the compressibility and hydrostatic balancc conditions, and a terrain-following ½r vertical coordinate system is adopted, where •r = (p -Ptop)/(P,• -Prop), D is prcssurc, P top is the pressure at the top of the model atmosphere, and p, is the prognostic surface pressure. The model vertical structure includes 17½r levels with the model top at 50 mbar. There arc six levels below •r: 0.79, six levels with Art • 0.1 between •r -0.7 and rr = 0.21, and five levels above rr -0.14.

We have applied the NCAR RegCM2 to the simulation of the interannual variability of precipitation over eastern Africa for the short-rains season by performing a set of experiments for the years 1982 to 1993. The model reproduced the observed interannual variability of precipitation in most of the years. The results show that remote factors play a dominant role in determining the precipitation anomalies. Interannual variability of precipitation over Tanzania is closely related to E1 Nifio events in their mature phase and sea surface temperature (SST) anomalies over the Indian and Atlantic Oceans. The southward shift of the Arabian High results in a southward shift of the zonal component of the Intertropical Convergence Zone (ITCZ), which is responsible for early onset of the rainy season (e.g., 1982 and 1986). The enhanced St. Helena High and weaker Mascarene High lead to the eastward shift of the meridional branch of the ITCZ for the wet years. Model simulations confirmed a strong positive correlation between precipitation anomalies over Lake Victoria and the warm E1 Nifio-Southern Oscillation events, by which enhanced moist westerly flow from the Atlantic Ocean and the mainly easterly flow from the Indian Ocean converge over Lake Victoria during wet years. The interannual variability of precipitation over Lake Victoria and the western Kenya Highlands (WKH) are strongly coupled. Positive precipitation anomalies over the WKH region are usually associated with weaker Arabian High and Mascarene High, which weaken the large-scale divergence over the WKH region and favor the development of convection. The interannual variability of precipitation over eastern Kenya Highlands (EKH) is not directly related to the E1 Nifio events, but the association with a warm SST anomaly pattern over the western Indian Ocean is evident during wet years. An E1 Nifio signal is, however, evident for wet years over the Turkana Channel, warm SST anomalies over the northern Indian Ocean contribute enhanced water vapor transport over the region. 1.

Since the summer of 2004, over 200 ozonesondes have been launched from the campuses of Rice University or the University of Houston (29.7 N, 95.3 W), each about 3 miles from downtown Houston. These sounding launches have been sponsored by NASA, the Shell Center for Sustainability of Rice University, and the Texas Commissions for Environmental Quality as part of a large effort to understand Houston s ozone problem. Data from these soundings have provided valuable insight into the seasonal and diurnal variations of the vertical ozone distribution and their relationship to changes in atmospheric conditions. In this presentation, we show annual and seasonal variability in the ozone profile, evidence for the impact of meteorological factors on the ozone profile, and comparisons of the ozonesonde data with TES and OMI retrievals.

The applicability of the Gravity Recovery and Climate Experiment (GRACE) to adequately represent broad-scale patterns of groundwater storage (GWS) variations and observed trends in groundwater-monitoring well levels (GWWL) is examined in the Canadian province of Alberta. GWS variations are derived over Alberta for the period 2002–2014 using the Release 05 (RL05) monthly GRACE gravity models and the Global Land Data Assimilation System (GLDAS) land-surface models. Twelve mean monthly GWS variation maps are generated from the 139 monthly GWS variation grids to characterize the annual GWS variation pattern. These maps show that, overall, GWS increases from February to June, and decreases from July to October, and slightly increases from November to December. For 2002–2014, the GWS showed a positive trend which increases from west to east with a mean value of 12 mm/year over the province. The resulting GWS variations are validated using GWWLs in the province. For the purpose of validati...

This paper examines WindSat wind retrievals from two perspectives. The first is a statistical analysis, comparing both WindSat and QuikSCAT to model output. The second is an analysis geared toward weather forecasters based on individual case studies.

The focus of our study is to investigate the feasibility and effectiveness of using Lattice Boltzmann Advection Diffusion Equation (LBM-ADE) to model and simulate ocean oil spill transport at the surface level. We present some preliminary results from a prototype model and simulation in limited scale (a sub area of Gulf of Mexico) with assimilation of real ocean current data from the Unified Wave Interface-Coupled Model (UWIN-CM). We validate our model in a benchmark study against GNOME, a tool developed and used by NOAA for ocean oil spill forecast, under two scenarios: (i) a Gaussian hill concentration using a linear ocean current with the analytical solution as a reference; (ii) a Gaussian hill concentration using real ocean current from (UWIN-CM). Our benchmark results in both cases show the LBM-ADE model solutions are very close to the targeted analytical and GNOME solutions with the same initial oil spill and location.

The cyclone 'Burevi' was recorded in the North Indian Ocean in the month of December 2020. In this study a numerical weather forecasting model WRF-ARW was used to forecast the track, intensity, and landfall location and time of the cyclone. The forecasting was performed as 16 consecutive model runs. Each forecast was initiated with different model initializations with a 12 hour time interval. For such initializations, the lateral boundary condition was set by the global forecast system data with a horizontal resolution of 0.25° x 0.25° at 3 hour time intervals. The forecasts were conducted up to 120 hours and evaluated using the data obtained from an international best track archive. The forecasted track was evaluated using root mean square error, direct positional error, along-track error and cross-track error. The intensity of the cyclone was evaluated by comparing the minimum sea level pressure and maximum forecasted 10-m wind speed obtained from the positions of the forecasted cyclone track. The WRF-ARW model was successful in forecasting the track and the intensity of the cyclone up to a lead time of 48 hours having a mean direct positional error of less than 106 km. This cyclone recorded three landfalls in Sri Lanka. The accuracy of the forecasted first landfall position and time were within acceptable range, but the second and third require further investigation.

Approximately 10,000 UV irradiance spectra resulting from 2 years of continuous measurements in Germany were used as a database to analyze the effect of cloudiness on spectral UV irradiance. Values of spectral cloud modification factors (CMF) were derived by modeling a corresponding clear‐sky irradiance spectrum for every UV measurement under cloudy conditions. The total set of CMF values was used to train neural networks using different sets of input data (parameter records) to describe the clouds, resulting in different, optimized, algorithms (CMF parameterizations). These different CMF parameterizations were evaluated by asking how the quality of the derived CMFs depended on the information content of different parameter records. It was shown that a visual description of cloudiness is not adequate to determine CMFs for an actual case (deviations of 50% and more), even if it was known whether or not the solar disk was obscured by clouds. Improvements for the determination of actua...

A new stepwise carbon cycle data assimilation system P. Peylin et al.: A new stepwise carbon cycle data assimilation system Carbon data assimila.on system Meteo. data prior param. calibration Optimized model parameters è Carbon fluxes & pools (values & uncertainties) Satellite data (NDVI) Atmos. conc. (CO2) Fossil fuel & biomass bur fluxes Flux tower (NEE, LE) Assimilation data Forcing data Ocean fluxes Validation data CO 2 vertical Profiles Forest & soil C stock change Satellite biomass data