Time-consistent estimators of 2D/3D motion of atmospheric layers from pressure images

In this paper, we face the challenging problem of estimation of time-consistent layer motion fields at various atmospheric depths. Based on a vertical decomposition of the atmosphere, we propose three different dense motion estimator relying on multi-layer dynamical models. In the first method, we propose a mass conservation model which constitutes the physical background of a multi-layer dense estimator. In the perspective of adapting motion analysis to atmospheric motion, we propose in this method a two-stage decomposition estimation scheme. The second method proposed in this paper relying on a 3D physical model for a stack of interacting layers allows us to recover a vertical motion information. In the last method, we use the exact shallow-water formulation of the Navier-Stokes equations to control the motion evolution across the sequence. This is done through a variational approach derived from data assimilation principle which combines the dynamical model and the pressure difference observations obtained from satellite images. The three methods use sparse pressure difference image observations derived from top of cloud images and classification maps. The proposed approaches are validated on synthetic example and applied to real world meteorological satellite image sequences

The Cooperative VAS Program with the Marshall Space Flight Center

Work was divided between the analysis/forecast model development and evaluation of the impact of satellite data in mesoscale numerical weather prediction (NWP), development of the Multispectral Atmospheric Mapping Sensor (MAMS), and other related research. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) Synoptic Scale Model (SSM) has progressed from a relatively basic analysis/forecast system to a package which includes such features as nonlinear vertical mode initialization, comprehensive Planetary Boundary Layer (PBL) physics, and the core of a fully four-dimensional data assimilation package. The MAMS effort has produced a calibrated visible and infrared sensor that produces imager at high spatial resolution. The MAMS was developed in order to study small scale atmospheric moisture variability, to monitor and classify clouds, and to investigate the role of surface characteristics in the production of clouds, precipitation, and severe storms

Diagnosing atmospheric motion vector observation errors for an operational high resolution data assimilation system

Atmospheric motion vectors (AMVs) are wind observations derived by tracking cloud or water vapour features in consecutive satellite images. These observations are incorporated into Numerical Weather Prediction (NWP) through data assimilation. In the assimilation algorithm, the weighting given to an observation is determined by the uncertainty associated with its measurement and representation. Previous studies assessing AMV uncertainty have used direct comparisons between AMVs with co-located radiosonde data and AMVs derived from Observing System Simulation Experiments (OSSEs). These have shown that AMV error is horizontally correlated with characteristic length scale up to 200 km. In this work, we take an alternative approach and estimate AMV error variance and horizontal error correlation using background and analysis residuals obtained from the Met Office limited area, 3 km horizontal grid length data assimilation system. The results show that the observation error variance profile ranges from 5.2 to 14.1 s m2s− 2, with the highest values occurring at high and medium heights. This is indicative that the maximum error variance occurs where wind speed and shear, in combination, are largest. With the exception of AMVs derived from the High Resolution Visible channel, the results show horizontal observation error correlations at all heights in the atmosphere, with correlation lengthscales ranging between 140 and 200 km. These horizontal lengthscales are significantly larger than current AMV observation thinning distances used in the Met Office high resolution assimilation

Pressure image assimilation for atmospheric motion estimation

The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations makes the recovery of atmospheric dynamics from satellite images sequences very difficult. In this report, we face the challenging problem of estimating physical sound and time consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose two dynamically consistent atmospheric motion estimators relying on different multi-layer dynamical models. Both estimators use a framework derived from data assimilation and are applied on noisy and incomplete pressure difference observations derived from satellite images. In the first model, dense pressure difference maps are reconstructed according to a shallow-water model on each cloud layer. While performing this reconstruction, the variational process estimates the average horizontal wind fields of the multi-layer model. The second model relies on a simplified vorticity-divergence form of the previous multi-layer shallow-water model. In this case, average horizontal motion fields are estimated for each layer without reconstructing pressure maps. While the simplified model is not as precise as the exact shallow-water model, the latter estimator exploits finer spatio-temporal image structures and succeeds in characterizing motion at smaller spatial scales. The performance of both methods is assessed on synthetic examples and on real world meteorological satellite image sequences

A particle filter to reconstruct a free-surface flow from a depth camera

We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This data assimilation scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone

Using models of dynamics for large displacement estimation on noisy acquisitions

The paper discusses the issue of motion estimation on noisy images displaying large displacements, due to high velocity values. ``Noisy'' means that the data contain either missing acquisitions on isolated points, regions, frames or noisy measures. Assuming the dynamics is partially accessible from heuristics and modeled, the objective is to include this knowledge in the computation of the solution even if large displacements occur from one frame to the next one and if the data are noisy. This is performed by Data Assimilation techniques which simultaneously solve an evolution equation and an observation equation. The evolution equation includes the partial knowledge on the dynamics. The observation equation describes the transport of image brightness and is written in a non-linear form in order to better characterize large displacements. The assimilation method is a weak 4D-Var algorithm, in which each component of the Data Assimilation system is associated to an error. We prove that the observation covariance matrix can be used to discard the noisy data during the computation of the solution letting the evolution equation estimate motion from adjacent frames on these pixels. The method is quantified on synthetic data and illustrated on oceanographic satellite images.Cet article traite du problème de l'estimation du mouvement sur des données bruitées montrant de grands déplacements engendrés par des vitesses élevées. Par données ``bruitées'' nous entendons des données qui contiennent à la fois des informations manquantes en des points isolés, des régions ou des plans image entiers et du bruit de mesure. En supposant que la dynamique de la séquence d'image peut être décrite par des heuristiques, le but est d'inclure cette connaissance dans le calcul de la solution et cela malgré la présence de vitesses élevées. Ceci est réalisé par assimilation de données en résolvant simultanément une équation d'évolution et une équation d'observation. L'équation d'évolution décrit imparfaitement la dynamique. L'équation d'observation décrit le transport de la luminosité par la vitesse et elle est écrite sous sa forme non linéaire afin de prendre en compte les grands déplacements. La méthode d'assimilation de données utilisée ici est le ``4DVar'' dans sa formulation faible et pour laquelle chaque composante du système à résoudre est associée à une erreur. Nous montrons que la matrice de covariance associée au modèle d'observation peut être utilisée pour éliminer du calcul de la solution les pixels qui contiennent une information bruitée. Pour ces pixels, l'équation d'évolution permet alors de calculer une solution admissible. La méthode proposée est évaluée sur des données synthétiques et appliquée sur des données océanographiques contenant de véritables données manquantes

On the representation error in data assimilation

Representation, representativity, representativeness error, forward interpolation error, forward model error, observation operator error, aggregation error and sampling error are all terms used to refer to components of observation error in the context of data assimilation. This paper is an attempt to consolidate the terminology that has been used in the earth sciences literature and was suggested at a European Space Agency workshop held in Reading in April 2014. We review the state-of-the-art, and through examples, motivate the terminology. In addition to a theoretical framework, examples from application areas of satellite data assimilation, ocean reanalysis and atmospheric chemistry data assimilation are provided. Diagnosing representation error statistics as well as their use in state-of-the-art data assimilation systems is discussed within a consistent framework

Data Assimilation in high resolution Numerical Weather Prediction models to improve forecast skill of extreme hydrometeorological events.

The complex orography typical of the Mediterranean area supports the formation, mainly during the fall season, of the so-called back-building Mesoscale Convective Systems (MCS) producing torrential rainfall often resulting into flash floods. These events are hardly predictable from a hydrometeorological standpoint and may cause significant amount of fatalities and socio-economic damages. Liguria region is characterized by small catchments with very short hydrological response time, and it has been proven to be very exposed to back-building MCSs occurrence. Indeed this region between 2011 and 2014 has been hit by three intense back-building MCSs causing a total death toll of 20 people and several hundred million of euros of damages. Building on the existing relationship between significant lightning activity and deep convection and precipitation, the first part of this work assesses the performance of the Lightning Potential Index, as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, for the back-building Mesoscale Convective System which hit Genoa city (Italy) in 2014. An ensemble of Weather Research and Forecasting simulations at cloud-permitting grid spacing (1 km) with different microphysical parameterizations is performed and compared to the available observational radar and lightning data. The results allow gaining a deeper understanding of the role of lightning phenomena in the predictability of back-building Mesoscale Convective Systems often producing flash flood over western Mediterranean complex topography areas. Despite these positive and promising outcomes for the understanding highly-impacting MCS, the main forecasting issue, namely the uncertainty in the correct reproduction of the convective field (location, timing, and intensity) for this kind of events still remains open. Thus, the second part of the work assesses the predictive capability, for a set of back-building Liguria MCS episodes (including Genoa 2014), of a hydro-meteorological forecasting chain composed by a km-scale cloud resolving WRF model, including a 6 hour cycling 3DVAR assimilation of radar reflectivity and conventional ground sensors data, by the Rainfall Filtered Autoregressive Model (RainFARM) and the fully distributed hydrological model Continuum. A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators, has been explored to drive the meteorological component of the proposed forecasting chain. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving first quantitative precipitation forecast, and, subsequently flash-floods occurrence prediction in case of back-building MCSs events. The third part of this work devoted the improvement of severe hydrometeorological events prediction has been undertaken in the framework of the European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aiming at investigating, new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites and Global Positioning System stations. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) to be used in NWP models runs operated at cloud resolving grid spacing . For this project two different use cases are analyzed: the Livorno flash flood of 9 Sept 2017, with a death tool of 9 people, and the Silvi Marina flood of 15 November 2017. Overall the results show an improvement of the forecast accuracy by assimilating the Sentinel-1 derived wind and soil moisture products as well as the Zenith Total Delay assimilation both from GPS stations and SAR Interferometry technique applied to Sentinel-1 data