Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.

Reconstructing Past Climate by Using Proxy Data and a Linear Climate Model

Thesis (Master's)--University of Washington, 2016-06

Data-Driven Wildfire Propagation Modeling with FARSITE-EnKF

The goal of this study is to provide a framework for future researchers to understand and use the FARSITE wildfire-forecasting model with data assimilation. Current wildfire models lack the ability to provide accurate prediction of fire front position faster than real-time. When FARSITE is coupled with a recursive ensemble filter, the data assimilation forecast method improves. The scope includes an explanation of the standalone FARSITE application, technical details on FARSITE integration with a parallel program coupler called OpenPALM, and a model demonstration of the FARSITE-Ensemble Kalman Filter software using the FireFlux I experiment by Craig Clements. The results show that the fire front forecast is improved with the proposed data-driven methodology than with the standalone FARSITE model.

Multiscale coupling and multiphysics approaches in earth sciences : theory

Measuring Earth material behaviour on time scales of millions of years transcends our current capability in the laboratory. We review an alternative path considering multiscale and multiphysics approaches with quantitative structure-property relationships. This approach allows a sound basis to incorporate physical principles such as chemistry, thermodynamics, diffusion and geometry-energy relations into simulations and data assimilation on the vast range of length and time scales encountered in the Earth. We identify key length scales for Earth systems processes and find a substantial scale separation between chemical, hydrous and thermal diffusion. We propose that this allows a simplified two-scale analysis where the outputs from the micro-scale model can be used as inputs for meso-scale simulations, which then in turn becomes the micro-model for the next scale up. We present two fundamental theoretical approaches to link the scales through asymptotic homogenisation from a macroscopic thermodynamic view and percolation renormalisation from a microscopic, statistical mechanics view.

Towards the Use of Radar Winds in Numerical Weather Prediction

Numerical weather prediction (NWP) models provide the basis for weather forecasting by simulating the evolution of the atmospheric state. A good forecast requires that the initial state of the atmosphere is known accurately, and that the NWP model is a realistic representation of the atmosphere. Data assimilation methods are used to produce initial conditions for NWP models. The NWP model background field, typically a short-range forecast, is updated with observations in a statistically optimal way. The objective in this thesis has been to develope methods in order to allow data assimilation of Doppler radar radial wind observations. The work has been carried out in the High Resolution Limited Area Model (HIRLAM) 3-dimensional variational data assimilation framework. Observation modelling is a key element in exploiting indirect observations of the model variables. In the radar radial wind observation modelling, the vertical model wind profile is interpolated to the observation location, and the projection of the model wind vector on the radar pulse path is calculated. The vertical broadening of the radar pulse volume, and the bending of the radar pulse path due to atmospheric conditions are taken into account. Radar radial wind observations are modelled within observation errors which consist of instrumental, modelling, and representativeness errors. Systematic and random modelling errors can be minimized by accurate observation modelling. The impact of the random part of the instrumental and representativeness errors can be decreased by calculating spatial averages from the raw observations. Model experiments indicate that the spatial averaging clearly improves the fit of the radial wind observations to the model in terms of observation minus model background (OmB) standard deviation. Monitoring the quality of the observations is an important aspect, especially when a new observation type is introduced into a data assimilation system. Calculating the bias for radial wind observations in a conventional way can result in zero even in case there are systematic differences in the wind speed and/or direction. A bias estimation method designed for this observation type is introduced in the thesis. Doppler radar radial wind observation modelling, together with the bias estimation method, enables the exploitation of the radial wind observations also for NWP model validation. The one-month model experiments performed with the HIRLAM model versions differing only in a surface stress parameterization detail indicate that the use of radar wind observations in NWP model validation is very beneficial.

Exploiting ground-based measurements of the Global Positioning System for numerical weather prediction

Data assimilation provides an initial atmospheric state, called the analysis, for Numerical Weather Prediction (NWP). This analysis consists of pressure, temperature, wind, and humidity on a three-dimensional NWP model grid. Data assimilation blends meteorological observations with the NWP model in a statistically optimal way. The objective of this thesis is to describe methodological development carried out in order to allow data assimilation of ground-based measurements of the Global Positioning System (GPS) into the High Resolution Limited Area Model (HIRLAM) NWP system. Geodetic processing produces observations of tropospheric delay. These observations can be processed either for vertical columns at each GPS receiver station, or for the individual propagation paths of the microwave signals. These alternative processing methods result in Zenith Total Delay (ZTD) and Slant Delay (SD) observations, respectively. ZTD and SD observations are of use in the analysis of atmospheric humidity. A method is introduced for estimation of the horizontal error covariance of ZTD observations. The method makes use of observation minus model background (OmB) sequences of ZTD and conventional observations. It is demonstrated that the ZTD observation error covariance is relatively large in station separations shorter than 200 km, but non-zero covariances also appear at considerably larger station separations. The relatively low density of radiosonde observing stations limits the ability of the proposed estimation method to resolve the shortest length-scales of error covariance. SD observations are shown to contain a statistically significant signal on the asymmetry of the atmospheric humidity field. However, the asymmetric component of SD is found to be nearly always smaller than the standard deviation of the SD observation error. SD observation modelling is described in detail, and other issues relating to SD data assimilation are also discussed. These include the determination of error statistics, the tuning of observation quality control and allowing the taking into account of local observation error correlation. The experiments made show that the data assimilation system is able to retrieve the asymmetric information content of hypothetical SD observations at a single receiver station. Moreover, the impact of real SD observations on humidity analysis is comparable to that of other observing systems.

TURBULENT PUMPING OF MAGNETIC FLUX REDUCES SOLAR CYCLE MEMORY AND THUS IMPACTS PREDICTABILITY OF THE SUN'S ACTIVITY

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. have shown that the dynamical memory of the solar dynamo mechanism governs predictability, and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum-allowing about five years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.

Evaluation of GODAS Using RAMA Mooring Observations from the Indian Ocean

We present a comparison of the Global Ocean Data Assimilation System (GODAS) five-day ocean analyses against in situ daily data from Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) moorings at locations 90 degrees E, 12 degrees N; 90 degrees E, 8 degrees N; 90 degrees E, 0 degrees N and 90 degrees E, 1.5 degrees S in the equatorial Indian Ocean and the Bay of Bengal during 2002-2008. We find that the GODAS temperature analysis does not adequately capture a prominent signal of Indian Ocean dipole mode of 2006 seen in the mooring data, particularly at 90 degrees E 0 degrees N and 90 degrees E 1.5 degrees S in the eastern India Ocean. The analysis, using simple statistics such as bias and root-mean-square deviation, indicates that standard GODAS temperature has definite biases and significant differences with observations on both subseasonal and seasonal scales. Subsurface salinity has serious deficiencies as well, but this may not be surprising considering the poorly constrained fresh water forcing, and possible model deficiencies in subsurface vertical mixing. GODAS reanalysis needs improvement to make it more useful for study of climate variability and for creating ocean initial conditions for prediction.

Dynamics of nonlinear error growth and season-dependent predictability of El Nino events in the Zebiak-Cane model

With the intermediate-complexity Zebiak-Cane model, we investigate the 'spring predictability barrier' (SPB) problem for El Nino events by tracing the evolution of conditional nonlinear optimal perturbation (CNOP), where CNOP is superimposed on the El Nino events and acts as the initial error with the biggest negative effect on the El Nino prediction. We show that the evolution of CNOP-type errors has obvious seasonal dependence and yields a significant SPB, with the most severe occurring in predictions made before the boreal spring in the growth phase of El Nino. The CNOP-type errors can be classified into two types: one possessing a sea-surface-temperature anomaly pattern with negative anomalies in the equatorial central-western Pacific, positive anomalies in the equatorial eastern Pacific, and a thermocline depth anomaly pattern with positive anomalies along the Equator, and another with patterns almost opposite to those of the former type. In predictions through the spring in the growth phase of El Nino, the initial error with the worst effect on the prediction tends to be the latter type of CNOP error, whereas in predictions through the spring in the decaying phase, the initial error with the biggest negative effect on the prediction is inclined to be the former type of CNOP error. Although the linear singular vector (LSV)-type errors also have patterns similar to the CNOP-type errors, they cover a more localized area than the CNOP-type errors and cause a much smaller prediction error, yielding a less significant SPB. Random errors in the initial conditions are also superimposed on El Nino events to investigate the SPB. We find that, whenever the predictions start, the random errors neither exhibit an obvious season-dependent evolution nor yield a large prediction error, and thus may not be responsible for the SPB phenomenon for El Nino events. These results suggest that the occurrence of the SPB is closely related to particular initial error patterns. The two kinds of CNOP-type error are most likely to cause a significant SPB. They have opposite signs and, consequently, opposite growth behaviours, a result which may demonstrate two dynamical mechanisms of error growth related to SPB: in one case, the errors grow in a manner similar to El Nino; in the other, the errors develop with a tendency opposite to El Nino. The two types of CNOP error may be most likely to provide the information regarding the 'sensitive area' of El Nino-Southern Oscillation (ENSO) predictions. If these types of initial error exist in realistic ENSO predictions and if a target method or a data assimilation approach can filter them, the ENSO forecast skill may be improved. Copyright (C) 2009 Royal Meteorological Society

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.

Mixed-layer water oscillations in tropical Pacific for ENSO cycle

The main modes of interannal variabilities of thermocline and sea surface wind stress in the tropical Pacific and their interactions are investigated, which show the following results. (1) The thermocline anomalies in the tropical Pacific have a zonal dipole pattern with 160 W as its axis and a meridional seesaw pattern with 6-8 degrees N as its transverse axis. The meridional oscillation has a phase lag of about 90 to the zonal oscillation, both oscillations get together to form the El Nino/La Nina cycle, which behaves as a mixed layer water oscillates anticlockwise within the tropical Pacific basin between equator and 12 degrees N. (2) There are two main patterns of wind stress anomalies in the tropical Pacific, of which the first component caused by trade wind anomaly is characterized by the zonal wind stress anomalies and its corresponding divergences field in the equatorial Pacific, and the abnormal cross- equatorial flow wind stress and its corresponding divergence field, which has a sign opposite to that of the equatorial region, in the off-equator of the tropical North Pacific, and the second component represents the wind stress anomalies and corresponding divergences caused by the ITCZ anomaly. (3) The trade winds anomaly plays a decisive role in the strength and phase transition of the ENSO cycle, which results in the sea level tilting, provides an initial potential energy to the mixed layer water oscillation, and causes the opposite thermocline displacement between the west side and east side of the equator and also between the equator and 12 degrees N of the North Pacific basin, therefore determines the amplitude and route for ENSO cycle. The ITCZ anomaly has some effects on the phase transition. (4) The thermal anomaly of the tropical western Pacific causes the wind stress anomaly and extends eastward along the equator accompanied with the mixed layer water oscillation in the equatorial Pacific, which causes the trade winds anomaly and produces the anomalous wind stress and the corresponding divergence in favor to conduce the oscillation, which in turn intensifies the oscillation. The coupled system of ocean-atmosphere interactions and the inertia gravity of the mixed layer water oscillation provide together a phase-switching mechanism and interannual memory for the ENSO cycle. In conclusion, the ENSO cycle essentially is an inertial oscillation of the mixed layer water induced by both the trade winds anomaly and the coupled ocean-atmosphere interaction in the tropical Pacific basin between the equator and 12 degrees N. When the force produced by the coupled ocean-atmosphere interaction is larger than or equal to the resistance caused by the mixed layer water oscillation, the oscillation will be stronger or maintain as it is, while when the force is less than the resistance, the oscillation will be weaker, even break.

最优变分伴随方法及在近岸水位资料同化中的应用

利用二维正压Princeton（POM）海洋模式模拟美国东海岸由表面风场产生的低频非潮汐水位变化特征。模式采用曲线正交网格，表面风场使用每3小时时间间隔、空间分辩率为48公里的EDAS（ETA Data Assimilation System）分析风场。沿岸潮汐观测资料（美国国家水位观测网）用来检验模式模拟结果以评估模拟结果的精度，这些观测资料也被用于动力同化模式中。在美国东海岸，表面风场是产生和引起低频水位及其变化的最重要的动力机制。观测资料的分析结果表明，非潮汐水位的最大振幅可达1以上，其对表面风场的响应大约滞后6～12小时左右。与观测资料相比，模式计算值的均方误差大约为8～12厘米，与观测资料之间的相关系统为0.54～0.91。为了获得更精确的模拟水位，提高实时水位预报系统的精度，本论文用伴随最优方法将尚岸水位观测资料同化到海洋动力模式中。建立一套实时水位预报同化系统。在该同化系统中，二维线性POM模式用作海洋基本模式（向前积分模式）。其伴随模式是通过拉格朗日（Lagrange）方法由离散的基本模式方程获得。观测水位与模拟水位之间的差被定义为价格函数。由于表面风场对美国东海岸低频水位的产生及其变化起着最重要的作用，表面风场的误差及风应力系统的误差都将引起模拟水位的误差。因此，在最优同化系统中，将表面风应力系统定义为控制变量。通过调整风应力系数改变风应力场，使模式计算的水位最好地接近观测值。有限记忆的准牛顿方法用于求解所形成的最优化问题。一致性“孪生”试验（假设的“观测数据”由模式本身产生，因此，控制变量的真实解是已知的）用来检验同化系统的正确性、有效性及收敛性。“孪生”试验结果表明该同化系统所求得的控制变量的解精确地收敛于真实解。在实测水位资料的同化试验中，设计了三种不同情况的试验，其对应的控制变量的个数分别为1，8，16。同化后模式结果表明，即使仅用一个控制变量，模式计算的水位比没有用同化技术的模式结果好，用16个控制变量的同化模式获得最好的结果，对这种情况，观测水位与同化模式的计算结果之间的相关系数在所有观测站均大于0.93，其计算结果的均方误差均小于5.3厘米。因此说同化模式的结果得到了很大的改进。实时水位预报结果表明，对于没有应用同化技术的预报系统，其预报水位均方误差在8.8～12厘米。同化技术对低频非潮汐水位预报结果的改进主要发生在前6小时。使预报的非潮汐水位的预报均方误差减少3厘米。

潮波模型的优化开边界方法研究

数值模式是潮波研究的一种有利手段，但在研究中会面临各种具体问题，包括开边界条件的确定、底摩擦系数和耗散系数的选取等。数据同化是解决这些问题的一种途径，即利用有限数量的潮汐观测资料对潮波进行最优估计，其根本目的是迫使模型预报值逼近观测值，使模式不要偏离实际情况太远。本文采用了一种优化开边界方法，沿着数值模型的开边界优化潮汐水位信息，目的是设法使数值解在动力约束的意义下接近观测值，获得研究区域的潮汐结果。边界值由指定优化问题的解来定，以提高模拟区域的潮汐精度，最优问题的解是基于通过开边界的能量通量的变化，处理开边界处的观测值与计算值之差的最小化。这里提供了辐射型边界条件，由Reid 和Bodine（本文简称为RB）推导，我们将采用的优化后的RB方法（称为ORB）是优化开边界的特殊情况。 本文对理想矩形海域（ E- E， N- N， 分辨率 ）进行了潮波模拟，有东部开边界，模式采用ECOM3D模式。对数据结果的误差分析采用，振幅平均偏差，平均绝对偏差，平均相对误差和均方根偏差四个值来衡量模拟结果的好坏程度。 需要优化入开边界的解析潮汐值本文采用的解析解由方国洪《海湾的潮汐与潮流》（1966年）方法提供，为验证本文所做的解析解和方文的一致，本文做了其第一个例子的关键值a,b,z,结果与其结果吻合的相当好。但略有差别，分析的可能原因是两法在具体迭代方案和计算机保留小数上有区别造成微小误差。另外，我们取m=20，得到更精确的数值，我们发现对前十项的各项参数值，取m=10,m=20各项参数略有改进。当然我们可以获得m更大的各项参数值。 同时为了检验解析解的正确性讨论m和l变化对边界值的影响，结果指出，增大m，m=20时，u的模最大在本身u1或u2的模的6%；m=100时，u的模最大在本身u1或u2的模的4%；m再增大，m=1000时，u的模最大在本身u1或u2的模的4%，改变不大。当l<1时， =0处u的模最大为2。当l=1时， =0处u的模最大为0.1，当l>1时，l越大，u的模越小，当l=10时，u的模最大为0.001，可以认为为0。 为检验该优化方法的应用情况，我们对理想矩形区域进行模拟，首先将本文所采用的优化开边界方法应用于30m的情况，在开边界优化入开边界得出模式解，所得模拟结果与解析解吻合得相当好，该模式解和解析解在整个区域上，振幅平均绝对偏差为9.9cm,相位平均绝对偏差只有4.0 ，均方根偏差只有13.3cm，说明该优化方法在潮波模型中有效。 为验证该优化方法在各种条件下的模拟结果情况，在下面我们做了三类敏感性试验： 第一类试验：为证明在开边界上使用优化方法相比于没有采用优化方法的模拟解更接近于解析解，我们来比较ORB条件与RB条件的优劣，我们模拟用了两个不同的摩擦系数，k分别为：0,0.00006。 结果显示，针对不同摩擦系数，显示在开边界上使用ORB条件的解比使用RB条件的解无论是振幅还是相位都有显著改善，两个试验均方根偏差优化程度分别为84.3%，83.7%。说明在开边界上使用优化方法相比于没有采用优化方法的模拟解更接近于解析解，大大提高了模拟水平。上述的两个试验得出， k=0.00006优化结果比k=0的好。 第二类试验，使用ORB条件确定优化开边界情况下，在东西边界加入出入流的情况，流考虑线性和非线性情况，结果显示，加入流的情况，潮汐模拟的效果降低不少，流为1Sv的情况要比5Sv的情况均方根偏差相差20cm,而不加流的情况只有0.2cm。线性流和非线性流情况两者模式解相差不大，振幅，相位各项指数都相近, 说明流的线性与否对结果影响不大。 第三类试验，不仅在开边界使用ORB条件，在模式内部也使用ORB条件，比较了内部优化和不优化情况与解析解的偏差。结果显示，选用不同的k,振幅都能得到很好的模拟，而相位相对较差。另外，在内部优化的情况下，考虑不同的k的模式解， 我们选用了与解析解相近的6个模式解的k,结果显示，不同的k,振幅都能得到很好的模拟，而相位较差。 总之，在开边界使用ORB条件比使用RB条件好，振幅相位都有大幅度改进，在加入出入流情况下，流的大小对模拟结果有影响，但线形流和非线性流差别不大。内部优化的结果显示，模式采用不同的k都能很好模拟解析解的振幅。

Contribution of the North Atlantic subtropical high to regional climate model (RCM) skill in simulating southeastern United States summer precipitation

© 2014, Springer-Verlag Berlin Heidelberg.This study assesses the skill of advanced regional climate models (RCMs) in simulating southeastern United States (SE US) summer precipitation and explores the physical mechanisms responsible for the simulation skill at a process level. Analysis of the RCM output for the North American Regional Climate Change Assessment Program indicates that the RCM simulations of summer precipitation show the largest biases and a remarkable spread over the SE US compared to other regions in the contiguous US. The causes of such a spread are investigated by performing simulations using the Weather Research and Forecasting (WRF) model, a next-generation RCM developed by the US National Center for Atmospheric Research. The results show that the simulated biases in SE US summer precipitation are due mainly to the misrepresentation of the modeled North Atlantic subtropical high (NASH) western ridge. In the WRF simulations, the NASH western ridge shifts 7° northwestward when compared to that in the reanalysis ensemble, leading to a dry bias in the simulated summer precipitation according to the relationship between the NASH western ridge and summer precipitation over the southeast. Experiments utilizing the four dimensional data assimilation technique further suggest that the improved representation of the circulation patterns (i.e., wind fields) associated with the NASH western ridge substantially reduces the bias in the simulated SE US summer precipitation. Our analysis of circulation dynamics indicates that the NASH western ridge in the WRF simulations is significantly influenced by the simulated planetary boundary layer (PBL) processes over the Gulf of Mexico. Specifically, a decrease (increase) in the simulated PBL height tends to stabilize (destabilize) the lower troposphere over the Gulf of Mexico, and thus inhibits (favors) the onset and/or development of convection. Such changes in tropical convection induce a tropical–extratropical teleconnection pattern, which modulates the circulation along the NASH western ridge in the WRF simulations and contributes to the modeled precipitation biases over the SE US. In conclusion, our study demonstrates that the NASH western ridge is an important factor responsible for the RCM skill in simulating SE US summer precipitation. Furthermore, the improvements in the PBL parameterizations for the Gulf of Mexico might help advance RCM skill in representing the NASH western ridge circulation and summer precipitation over the SE US.

Reconstrução climática para Portugal através de downscaling dinâmico

Apresenta-se uma avaliação de vários métodos de downscaling dinâmico. Os métodos utilizados vão desde o método clássico de aninhar um modelo regional nos resultados de um modelo global, neste caso as reanálises do ECMWF, a métodos propostos mais recentemente, que consistem em utilizar métodos de relaxamento Newtoniano de forma a fazer tender os resultados do modelo regional aos pontos das reanálises que se encontram dentro do domínio deste. O método que apresenta melhores resultados envolve a utilização de um sistema variacional de assimilação de dados de forma a incorporar dados de observações com resultados do modelo regional. A climatologia de uma simulação de 5 anos usando esse método é testada contra observações existentes sobre Portugal Continental e sobre o oceano na área da Plataforma Continental Portuguesa, o que permite concluir que o método desenvolvido é apropriado para reconstrução climática de alta resolução para Portugal Continental.