Optimal Interpolation Technique Research Articles

Multiscale Observations of Deep Convection in the Northwestern Mediterranean Sea During Winter 2012–2013 Using Multiple Platforms

AbstractDuring winter 2012–2013, open‐ocean deep convection which is a major driver for the thermohaline circulation and ventilation of the ocean, occurred in the Gulf of Lions (Northwestern Mediterranean Sea) and has been thoroughly documented thanks in particular to the deployment of several gliders, Argo profiling floats, several dedicated ship cruises, and a mooring array during a period of about a year. Thanks to these intense observational efforts, we show that deep convection reached the bottom in winter early in February 2013 in a area of maximum 28 ± 3 . We present new quantitative results with estimates of heat and salt content at the subbasin scale at different time scales (on the seasonal scale to a 10 days basis) through optimal interpolation techniques, and robust estimates of the deep water formation rate of 2.0 . We provide an overview of the spatiotemporal coverage that has been reached throughout the seasons this year and we highlight some results based on data analysis and numerical modeling that are presented in this special issue. They concern key circulation features for the deep convection and the subsequent bloom such as Submesoscale Coherent Vortices (SCVs), the plumes, and symmetric instability at the edge of the deep convection area.

Development and assessment of the SMAP enhanced passive soil moisture product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9km grid instead of a 36km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015–October 30, 2016. It was found that the performance of the enhanced 9km L2_SM_P_E is equivalent to that of the standard 36km L2_SM_P, attaining a retrieval uncertainty below 0.040m3/m3 unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3.9 and 6.5 °C for 2050s and 2080s relative to 1990s in Ontario, respectively; Cooling degree days and hot days will significantly increase over southern Ontario and heating degree days and cold days will significantly decrease in northern Ontario. Annual total precipitation will increase over Ontario and heavy precipitation events will increase as well. These results are consistent with conclusions in many other studies in the literature.

Characterization of monochlorobenzene contamination in soils using geostatistical interpolation and 3D visualization for agrochemical industrial sites in southeast China

Abstract Persistent organic pollutants (POPs) originating from agrochemical industries have become an urgent environmental problem worldwide. Ordinary kriging, as an optimal geostatistical interpolation technique, has been proved to be sufficiently robust for estimating values with finite sampled data in most of the cases. In this study, ordinary kriging interpolation integrate with 3D visualization methods is applied to characterize the monochlorobenzene contaminated soil for an agrochemical industrial site located in Jiangsu province. Based on 944 soil samples collected by Geoprobe 540MT and monitored by SGS environmental monitoring services, 3D visualization in terms of the spatial distribution of pollutants in potentially contaminated soil, the extent and severity of the pollution levels in different layers, high concentration levels and isolines of monochlorobenzene concentrations in this area are provided. From the obtained results, more information taking into account the spatial heterogeneity of soil area will be helpful for decision makers to develop and implement the soil remediation strategy in the future.

Optimal interpolation analysis of high‐latitude ionospheric Hall and Pedersen conductivities: Application to assimilative ionospheric electrodynamics reconstruction

AbstractWe have developed a new optimal interpolation (OI) technique to estimate complete high‐latitude ionospheric conductance distributions from Defense Meteorological Satellite Program particle data. The technique combines particle precipitation‐based calculations of ionospheric conductances and their errors with a background model and its error covariance (modeled with empirical orthogonal functions) to infer complete distributions of the high‐latitude ionospheric conductances. We demonstrate this technique for the 26 November through 2 December 2011 period and analyze a moderate geomagnetic storm event on 30 November 2011. Quantitatively and qualitatively, this new technique provides better ionospheric conductance specification than past statistical models, especially during heightened geomagnetic activity. We provide initial evidence that auroral images from the Defense Meteorological Satellite Program Special Sensor Ultraviolet Spectrographic Imager instrument can be used to further improve the OI conductance maps. Our OI conductance patterns allow assimilative mapping of ionospheric electrodynamics reconstructions driven separately by radar and satellite magnetometer observations to be in closer agreement than when other, commonly used, conductance models are applied. This work (1) supports better use of the diverse observations available for high‐latitude ionospheric electrodynamics specification and (2) supports the Cousins et al. (2015b) assertion that more accurate models of the ionospheric conductance are needed to robustly assimilate ground‐ and space‐based observations of ionospheric electrodynamics. We find that the OI conductance distributions better capture the dynamics and locations of discrete electron precipitation that modulate the coupling of the magnetosphere‐ionosphere‐thermosphere system.

Reconstruction of transports through the Strait of Gibraltar from limited observations

Observing the water transports through the Strait of Gibraltar is a difficult task. Here we present a methodology aimed to obtain the inflow, outflow and net transport of water from the limited set of available observations, currently consisting of an upward looking ADCP deployed at Espartel sill, two tide gauges located at each side of the Strait and radars monitoring the surface velocities. More precisely, we reconstruct the velocity field over a vertical section across the Strait using a reduced order optimal interpolation technique fed with the spatial covariance patterns deduced from high resolution numerical simulations. As a first step we carry out some sensitivity experiments with synthetic data that demonstrate the high potential of the approach. The reconstruction methodology can reproduce very satisfactorily the variability of the transports with estimated correlations for the inflow, outflow and net over 0.9 in all the cases and estimated RMS errors of 0.03, 0.08 and 0.05 Sv, respectively. However, we have also found that the reconstruction is sensible to bias problems, mostly due to the sensitivity of the method to the differences between the statistics of the actual and modeled velocity profiles. The sensitivity experiments have been used to tune the parameters of the method and a reconstruction of actual monthly transports has been performed for the period 2004–2010 along with an estimate of the associated uncertainty. This reconstruction provides for the first time a multiannual time series of the inflow and the net transports solely based on in situ observations. Therefore it can be used as an independent estimate for the validation of numerical models and surface freshwater fluxes in the Mediterranean.

How well would modern‐day oceanic property distributions be known with paleoceanographic‐like observations?

Author(s): Gebbie, G; Streletz, GJ; Spero, HJ | Abstract: Compilations of paleoceanographic observations for the deep sea now contain a few hundred points along the oceanic margins, mid-ocean ridges, and bathymetric highs, where seawater conditions are indirectly recorded in the chemistry of buried benthic foraminiferal shells. Here we design an idealized experiment to test our predictive ability to reconstruct modern-day seawater properties by considering paleoceanographic-like data. We attempt to reconstruct the known, modern-day global distributions by using a state estimation method that combines a kinematic tracer transport model with observations that have paleoceanographic characteristics. When a modern-like suite of observations (Θ, practical salinity, seawater δ18O, δ13CDIC, PO4, NO3, and O2) is used from the sparse paleolocations, the state estimate is consistent with the withheld data at all depths below 1500 m, suggesting that the observational sparsity can be overcome. Physical features, such as the interbasin gradients in deep δ13CDIC and the vertical structure of Atlantic δ13CDIC, are accurately reconstructed. The state estimation method extracts useful information from the pointwise observations to infer distributions at the largest oceanic scales (at least 10,000 km horizontally and 1500 m vertically) and outperforms a standard optimal interpolation technique even though neither dynamical constraints nor constraints from surface boundary fluxes are used. When the sparse observations are more realistically restricted to the paleoceanographic proxy observations of δ13C, δ18O, and Cd/Ca, however, the large-scale property distributions are no longer recovered coherently. At least three more water mass tracers are likely needed at the core sites in order to accurately reconstruct the large-scale property distributions of the Last Glacial Maximum.

Impact of Assimilating SARAL/AltiKa SWH in SWAN Model During Indian Ocean Tropical Cyclone Phailin

The focus of the study is assimilation of significant wave height (SWH) obtained from first Ka-band altimeter AltiKa onboard SARAL, a collaborative mission of the Indian Space Research Organization and Centre National d'Etudes Spatiales, France. The state-of-the art coastal wave model simulating waves near-shore has been used for this purpose. The well-tested optimal interpolation technique is adopted for this assimilation. A distinguishing feature of this study is the test of this assimilation scheme with SARAL/AltiKa data during the occurrence of a recent severe Cyclone Phailin in the Indian Ocean with its associated extreme waves. Before proceeding to assimilation per se, SARAL/AltiKa SWH has been validated using in situ as well as other satellite data. Apart from validation, this intercomparison also served the purpose of providing the needed error covariance to be used in assimilation. The supremacy of an assimilation run over a parallel control run without assimilation has been judged by comparing the results with Indian Ocean buoy observations and also with Jason-2 observations. The statistics of the validation of the assimilation run has been found to be extremely encouraging.

Assessment of the Wave Energy in the Black Sea Based on a 15-Year Hindcast with Data Assimilation

The principal target of the present work is to assess the wave energy potential in the Black Sea, identifying also some relevant energetic features and possible patterns. A wave prediction system based on the Simulating Waves Nearshore model (SWAN) has been implemented and intensively tested in the entire sea basin. Moreover, considering an optimal interpolation technique, an assimilation scheme of the satellite data has been developed, leading to a visible improvement of the wave model predictions in terms of significant wave heights and, consequently, also in terms of wave power. Using this wave prediction system with data assimilation, simulations have been performed for a 15-year period (1999–2013). Considering the results of this 15-year wave hindcast, an analysis of the wave energy conditions in the basin of the Black Sea has been carried out. This provided a more comprehensive picture concerning the wave energy patterns in the coastal environment of the Black Sea focused on the average wave conditions that might be expected in this sea. Following the results presented, it can be concluded that the wave energy extraction in the Black Sea can become an issue of interest, especially from the perspective of the hybrid solutions.

Impact of SARAL/AltiKa-Derived Sea Level Anomaly in a Data Assimilative Ocean Prediction System for the Indian Ocean

The impact of SARAL/AltiKa derived sea level anomaly (SLA) has been studied by assimilating it along with Jason-2 and Cryosat-2 SLA in the Princeton Ocean model (POM) using ensemble optimal interpolation (EnOI) technique. For isolating the extra benefit brought by SARAKL/Altika, a parallel run with assimilation of only Jason-2 and Cryosat-2 SLA has also been conducted. The importance of SARAL SLA in a data assimilative ocean prediction system has been evaluated with special emphasis on the improvement in thermocline depth, depth of the 20° isotherm, subsurface temperature and currents. Comparison with RAMA buoy has shown a positive impact of up to 13% for 20°C isotherm and up to 17% for thermocline depth after assimilating SARAL SLA. An overall improvement in temperature profile is also observed when compared with analogous profiles from RAMA buoys and Argo floats. Improvement in zonal currents away from the equator has also been noticed.

Analysis of SARAL/AltiKa Wind and Wave over Indian Ocean and its Real-time Application in Wave Forecasting System at ISRO

The current study aims to analyze the wind and wave parameters over Indian Ocean region obtained from first Ka –band altimeter AltiKa onboard SARAL, a collaborative mission of Indian Space Research Organization (ISRO) and Centre National d'Etudes Spatiales (CNES), France. It also demonstrates a real time application of SARAL data by assimilating the wave height in a wave model operational at the Space Applications Centre, ISRO. State-of–the art coastal wave model Simulating Wave Near shore (SWAN) is used for this purpose. The well-tested optimal interpolation technique is adopted for assimilation. Before proceeding to the assimilation per se, SARAL/AltiKa Wind and Significant Wave Height (SWH) have been validated using in- situ observations and WAVEWATCH III model. Apart from assessment of wind and wave data quality, this also served the purpose of providing error covariance to be used in assimilation. Supremacy of the assimilation run over parallel control run without assimilation has been judged by comparing the results with buoy observations at Indian National Centre for Ocean Information System (INCOIS). The statistics of validation of the assimilation run has been found to be extremely encouraging and interesting.

Quality Assessment of HF Radar–Derived Surface Currents Using Optimal Interpolation

AbstractThis study investigates the applicability of the optimal interpolation (OI) method proposed by Kim et al. for estimating ocean surface currents from high-frequency radar (HFR) in the northeastern Chukchi Sea, where HFR siting is dictated by power availability rather than optimal locations. Although the OI technique improves data coverage when compared to the conventional unweighted least squares fit (UWLS) method, biased solutions can emerge. The quality of the HFR velocity estimates derived by OI is controlled by three factors: 1) the number of available incorporating radials (AR), 2) the ratio of the incorporating radials from multiple contributing site locations [ratio of overlapping radial velocities (ROR) or radar geometry], and 3) the positive definiteness [condition number (CN)] of the correlation matrix. Operationally, ROR does not require knowledge of the angle covariance matrix used to compute the geometric dilution of precision (GDOP) in the UWLS method and can be computed before site selection to optimize coverage or after data processing to assess data quality when applying the OI method. The Kim et al. method is extended to examine sensitivities to data gaps in the radial distribution and the effects on OI estimates.

Optimal combination of gridded and ground-observed solar radiation data for regional solar resource assessment

Proper quantification of the available solar resource is essential for the diverse development phases of any solar power plant. At local scale, the solar resource is best assessed from ground radiometric stations. At regional-to-continental scales, however, satellite-based techniques are currently the most suitable approach. Solar radiation evaluation using numerical weather prediction (NWP) models presents some advantages over satellite-based techniques. Nonetheless, gridded solar radiation estimates using either satellite-based or NWP-based techniques still produce biased estimates which are often much higher for the latter. Therefore, a correction needs to be applied before these gridded values are usable for solar applications, especially for NWP-based estimates. This contribution introduces an original method based on the optimal interpolation technique to adjust gridded solar radiation estimates consistently with concomitant radiometric ground observations. The method’s performance is demonstrated using NWP-based gridded estimates with 10-km spacing of global and direct monthly irradiation data during the 10-year period from 2003 to 2012 over continental Spain and the Balearic Islands. It is shown that the proposed methodology produces adjusted gridded values that are (or nearly always are, in the case of direct irradiation) within the expected measurement uncertainty of the ground observations, provided a sufficiently large number of observations is available for correction. For the studied region and these NWP-based gridded datasets, our findings suggest that a homogeneous mean distance between ground observations of 100–150km can result in unbiased gridded estimates.

Reconstructing pre-erosion topography using spatial interpolation techniques: A validation-based approach

Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, and knowledge of the original surface is a condition for quantifying the volume of eroded material. Although any reconstruction implies assuming that the resulting surface reflects the original topography, past works have been dominated by linear interpolation methods, incapable of generating curved surfaces in areas with no data or values outside the range of variation of inputs. In spite of these limitations, impossibility of validation has led to the assumption of surface representativity never being challenged.In this paper, a validation-based method is applied in order to define the optimal interpolation technique for reconstructing pre-erosion topography in a given study area. In spite of the absence of the original surface, different techniques can be nonetheless evaluated by quantifying their capacity to reproduce known topography in unincised locations within the same geomorphic contexts of existing erosive landforms. A linear method (Triangulated Irregular Network, TIN) and 23 parameterizations of three distinct Spline interpolation techniques were compared using 50 test areas in a context of research on large gully dynamics in the South of Portugal. Results show that almost all Spline methods produced smaller errors than the TIN, and that the latter produced a mean absolute error 61.4% higher than the best Spline method, clearly establishing both the better adjustment of Splines to the geomorphic context considered and the limitations of linear approaches.The proposed method can easily be applied to different interpolation techniques and topographic contexts, enabling better calculations of eroded volumes and denudation rates as well as the investigation of controls by antecedent topographic form over erosive processes.

Improving the PM10 estimates of the air quality model AURORA by using Optimal Interpolation

This paper presents the results of using a data assimilation technique known as Optimal Interpolation (OI) for improving the PM10 estimates of the air quality model AURORA. Ground-based measurements provided by IRCEL (the Belgian Interregional Environment Agency) have been used in the data assimilation process. AURORA has been set up to cover a domain consisting of Belgium, Luxemburg, and small parts of France, Germany and the Netherlands with a grid resolution of 5×5 km2. In this study, the characterization of the error covariances between the grid-cells has been done by using the Hollingsworth-Lönnberg method. The simulation results show that the optimal interpolation technique manages to significantly reduce the error of the PM10 estimates, which indirectly leads to an improvement of the PM2.5 field.

Optimal scale polynomial interpolation technique for obtaining periodic solutions to the Duffing oscillator

A novel optimal scale polynomial interpolation method (OSPIM) is proposed to attack the Duffing oscillator. This method is based on the ideas of multi-scaling and equilibrated matrix, such that the condition number of the coefficient matrix of the polynomial interpolation is minimized. The OSPIM can eliminate the Runge phenomenon, which occurs in the conventional polynomial interpolation, and is well suited for solving nonlinear oscillatory systems. In addition, we further alleviate the ill-posedness of polynomial interpolation by proposing a half-order technique, with which one can use a \(m\)-order polynomial to interpolate as many as \(2m+1\) points. We then employ the half-order OSPIM, i.e., OSPIM(H), as a trial function in conjunction with the simple point-collocation method, to solve the nonlinear Duffing equation. Moreover, the differential transformation method is used for the first time to solve a forced Duffing oscillator to compare with the present method. Finally, illustrative examples verify the accuracy and efficiency of the present method.

An OGCM assessment of blended OSCAT winds

Ensemble optimal interpolation technique has been used to blend winds for the years 2011–2012, derived by OSCAT, a scatterometer onboard the satellite Oceansat‐2, with winds from two different numerical weather prediction (NWP) centers, namely, National Center for Environmental Prediction (NCEP) and European Centre for Medium Range Weather Forecast (ECMWF). These blended winds, when compared with buoy winds, show higher correlations and lower root‐mean‐square errors (RMSE) than the standalone NWP winds. Analysis of divergence and curl of wind stresses also suggests that the blending is physically realistic. Comparisons of blended winds with buoy winds and analysis of divergence and curls as also the subsequent spectral analysis show that the blended ECMWF wind is the best among all the wind products. Consequent assessment of the impact of blended ECMWF wind product has been done using an ocean general circulation model (OGCM). The technique of empirical orthogonal function (EOF) analysis has been used to assess the impact on sea level and surface currents. The dominant modes of simulated variability in the case of forcing by blended winds are found to be more faithfully reproducing the well‐known features of global ocean circulation. Validation of the depth of the 20°C isotherm (D20) also establishes the relative superiority of the blended wind product.

Satellite-based PM concentrations and their application to COPD in Cleveland, OH.

A hybrid approach is proposed to estimate exposure to fine particulate matter (PM(2.5)) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time-space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM(2.5) data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM(2.5) between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time-space Kriging to compute exposure at a given location and time using the predicted PM(2.5). Daily estimates of PM(2.5) were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (∼79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time-space lagged PM(2.5) exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM(2.5) exposure within 9 days and 0.05° (∼5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM(2.5) >15.4 μg/m(3)) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.

Regional climate models downscaling in the Alpine area with multimodel superensemble

Abstract. The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.

A Modified Optimal Interpolation Technique for Vector Retrieval for Coherent Doppler LIDAR

Several modifications to the optimal interpolation (OI) technique for light-detection-and-ranging vector retrieval are proposed and shown to result in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. A validation of the modified OI technique is presented, comparing the retrievals with the original technique in each case. The modified technique is able to perform retrievals with better accuracy, preserves local information better, and compares well with tower measurements.