• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Surface Weather Parameters Forecasting Using Analog Ensemble Method over the Main Airports of Morocco

    2023-01-16 12:06:24
    Journal of Meteorological Research 2022年6期

    ABSTRACT Surface weather parameters detain high socioeconomic impact and strategic insights for all users, in all domains(aviation, marine traffic, agriculture, etc.). However, those parameters were mainly predicted by using deterministic numerical weather prediction (NWP) models that include a wealth of uncertainties. The purpose of this study is to contribute in improving low-cost computationally ensemble forecasting of those parameters using analog ensemble method (AnEn) and comparing it to the operational mesoscale deterministic model (AROME) all over the main airports of Morocco using 5-yr period (2016–2020) of hourly datasets. An analog for a given station and forecast lead time is a past prediction, from the same model that has similar values for selected predictors of the current model forecast. Best analogs verifying observations form AnEn ensemble members. To picture seasonal dependency, two configurations were set; a basic configuration where analogs may come from any past date and a restricted configuration where analogs should belong to a day window around the target forecast. Furthermore, a new predictors weighting strategy is developed by using machine learning techniques (linear regression, random forest, and XGBoost). This approach is expected to accomplish both the selection of relevant predictors as well as finding their optimal weights,and hence preserve physical meaning and correlations of the used weather variables. Results analysis shows that the developed AnEn system exhibits a good statistical consistency and it significantly improves the deterministic forecast performance temporally and spatially by up to 50% for Bias (mean error) and 30% for RMSE (root-mean-square error) at most of the airports. This improvement varies as a function of lead times and seasons compared to the AROME model and to the basic AnEn configuration. The results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.

    Key words: analog ensemble, machine learning, surface weather parameters, ensemble forecasting, AROME (Applications de la Recherche à l’Opérationnel à Méso-Echelle), predictors weighting strategy

    1. Introduction

    In most national meteorological services, numerical weather prediction (NWP) is mainly based on determinism. In this doctrine, given an initial state of the atmosphere, its evolution numerically leads to a unique prediction scenario. However, deterministic weather forecasts show several uncertainties, occasioned by several error sources related to model formulation (Orrell et al.,2001), initial state (PaiMazumder and M?lders, 2009),physical parameterization (Palmer, 2001), and lateral boundary conditions (Eckel and Mass, 2005). In front of those limitations, ensemble prediction takes advantage of all these uncertainty sources to construct multiple forecasts starting from slightly different but equally-probable initial states (Leith, 1974). Several national weather services around the world use ensemble prediction systems such as the NCEP (Toth and Kalnay, 1993, 1997;Toth, 2001; Zhu, 2005; Zhou et al., 2017), ECMWF(Buizza, 2008; Buizza and Richardson, 2017), the Meteorological Service of Canada (Buizza et al., 2005), and Météo-France (Vié et al., 2011). Those systems exhibit a high efficiency helping forecasters of those centers in the decision-making process. In order to construct ensemble prediction systems, several techniques were deployed such as perturbative methods that depend on atmospheric flow and based on perturbations in sub-spaces where initial condition errors grow faster, for example: breeding vectors (Toth and Kalnay, 1993, 1997) and singular vectors (Molteni et al., 1996). Recently, new data assimilation methods emerged and are included in ensemble systems for NCEP (Whitaker et al., 2008; Zhou et al., 2017)and for ECMWF (Hamill et al., 2000, 2011; Buizza et al.,2008).

    Among those techniques, analog ensemble (hereafter AnEn) forecasting is considered as an intuitive and low cost method of generating ensemble members. The fundamental idea of this method is to construct an ensemble forecast from a set of past observations of the variable to be predicted, neatly selected from a historical training dataset. For a given location, the most similar past forecasts to the current prediction are identified and their associated past observations are nominated as members of the analog forecast ensemble. Thus, the availability of records of NWP deterministic forecasts is a cornerstone to the application of this method. Analog ensemble method’s objective is to discern all the past weather conditions where the error probability density function was similar.Once those conditions are distinguished, one can use the past observation errors to deduce the future errors probability density function. Analog ensemble method was theoretically introduced firstly by Hamill and Whitaker(2006), and then, it was successfully applied by Delle Monache et al. (2013), hereafter DM13, to generate probabilistic prediction of wind at 10 m and temperature at 2 m. Then, several successful applications of this technique chained mainly in renewable energy for wind and solar energy (Alessandrini et al., 2015; Davò et al.,2016), energy load (Alessandrini et al., 2015), tropical cyclones intensity (Alessandrini et al., 2018), air quality predictions (Djalalova et al., 2015; Delle Monache et al.,2018), dynamical forecast errors correction (Yu et al.,2014; Gong et al., 2016), and also in the field of data assimilation (Lguensat et al., 2017). However, the previous studies focused on a few surface weather parameters.These parameters represent an important aspect of meteorology because they are the basis for all weather safety messages, weather forecasts, and weather warnings worldwide. Thus, extending research studies to assess the potential of AnEn method for more surface weather forecasting (e.g., 2-m relative humidity and mean sea level pressure, zonal and meridional 10-m wind components)is still needed.

    It is evident that the likelihood of finding good analogs depends strongly on the similarity metric and also the neighboring criteria (time window) and the weighting strategy applied to the predictors that exhibit correlations to the predictand. Several weighting algorithms of different philosophies were explored. The ultimate goal of these algorithms is to attribute a weight to each predictor in the AnEn method (Delle Monache et al., 2013).Junk et al. (2015) developed a static and dynamic weighting strategy where predictors weights are defined by probabilistic score minimization process over all plausible weights combinations, given discrete weight values. These strategies revealed an improvement of up to 20% in the performance of AnEn compared to the basic algorithm of DM13 where weights were initiated to 1(all predictors have the same weight). On the other hand,Gensler et al. (2016) introduced a weight optimization wrapper technique based on the Nelder–Mead simplex algorithm (Lagarias et al., 1998). The main idea of this approach is to find the best weights combination selected randomly, which minimizes the root-mean-square error (RMSE) between AnEn forecasts and actual measurements. This method outperforms the DM13 strategy by 10% to 21%. Finally, Tuba and Bottyán (2018) explored another family of weighting methods, based on AHP(Analytic Hierarchy Process) decision-making algorithm(Saaty, 1987). This method reproduces the best weights combination based on eigenvector decomposition of a matrix of ratios. Those ratios translate the importance of each variable relatively to others. Weights in this method are represented by the normalized eigenvector associated with the maximal eigenvalue. A novelty in our study is the usage of machine learning techniques to compute the predictors weights. This strategy is investigated and evaluated against traditional statistical method (linear regression). In addition, in this study we extend the target predictands and predictors to eight surface weather parameters: temperature and relative humidity at 2 m, surface and mean sea level pressure, wind speed and direction at 10 m, and also the zonal and meridional 10-m wind components.

    It is to mention that this work is the first AnEn application using mesoscale operational model AROME (Applications de la Recherche à l’Opérationnel à Méso-Echelle) (Seity et al., 2011). The latter is widely used among an important part of the scientific community, and it is also used operationally in many countries, including Morocco (Hdidou et al., 2020). In the literature, AROME model was subject of many scientific contributions especially in targeting atmospheric convective activity and data assimilation (Degrauwe et al., 2016; Sahlaoui et al.,2020). However, some scientific studies addressed the AROME-based ensemble forecasting using other approaches than AnEn (Vié et al., 2011; Bouttier et al.,2016; Bousquet et al., 2020). Thus, our research aims to apply AnEn to AROME outputs, particularly regarding the main surface weather parameters. In addition, the current operational AROME model was coupled with the global NWP ARPEGE (Action de Recherche Petite Echelle Grande Echelle) model (Courtier et al., 1991)since 2020 instead of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model(Bubnová et al., 1995) since 2015. The impact of lateral boundary conditions change on AnEn performances will be also assessed in this study since AnEn leverages a single deterministic NWP model to produce ensemble forecasting for surface weather parameters.

    In this study, we propose an in-depth analysis of different configurations applied to AROME-based analog ensemble forecasting. To test the performance of the developed ensemble forecasting system and to determine the optimal configuration, several experiments using different scenarios are performed. The experiments use more meteorological variables as predictors and provide a 25-member analog ensemble forecast. The results analysis is investigated using 15 meteorological stations,located mainly in airports, in Morocco over 1-yr period.The deterministic AROME forecasts are used as a reference in order to better understand the analog method impact on the surface weather forecasts.

    This paper is organized as follows. Section 2 describes the study domain and datasets with a brief overview on the used NWP model AROME. Section 3 is dedicated to the methodology and the experimental design.Results analysis based on verification scores of the global performance is detailed in Section 4 regarding the spatiotemporal distribution and also the season dependency.This paper ends with a discussion section and conclusions, which summarize the main findings of this research.

    2. Study domain and datasets

    The mesoscale limited area model used in this study is the AROME-Morocco model (Hdidou et al., 2020),which has been in operational use at the Moroccan National Meteorological Service since 2015. This model was developed by Météo-France (Seity et al., 2011) and is being maintained and further refined in collaboration between the meteorological institutes belonging to the ALADIN-HIRLAM consortia (http://www.umr-cnrm.fr/accord/).

    The AROME canonical model configuration has been developed to run in the convection-permitting resolutions starting from 2.5-km resolution. Its setup is described by Seity et al. (2011) and Brousseau et al. (2016).Its physical parameterizations come mostly from the Méso-NH research model (Lafore et al., 1998) whereas the dynamical core is the non-hydrostatic ALADIN core(Bubnová et al., 1995). The coupling between the atmosphere and the underlying surface was based on the SURFEX system (www.umr-cnrm.fr/surfex/).

    AROME-Morocco covers the Moroccan kingdom and the south of Spain with a horizontal resolution of 2.5 km and 90 vertical levels, with the lowest level at about 5 m above the ground. The lateral boundary conditions are provided by hourly forecasts from the ALADIN model(Bubnová et al., 1995) and from the ARPEGE model(Courtier et al., 1991) since January 2020. AROME-Morocco runs operationally twice a day with a 48-h forecast range.

    The dataset used in this work comprises eight meteorological parameters: temperature (T2m) and relative humidity (RH2m) at 2 m, mean sea level pressure (MSLP),surface pressure (SURFP), wind speed (WS10m) and direction (WD10m) at 10 m, zonal (ZW10m) and meridional(MW10m) components of 10-m wind. The study period covers 5 yr (2016–2020). The hourly forecasts of these parameters are extracted from midnight AROME run outputs up to 24 h of every day. Similarly, the same parameters are made available hourly from SYNOP (surface synoptic observations) messages of 15 national airports covering the Moroccan territory (see Fig. 1) and are spatially distributed over topographically heterogeneous terrain. It should be noted here that airports in mountainous regions are excluded from this study because the geopotential parameter is archived instead of MSLP.

    Morocco is a country in the subtropical zone of North West Africa. It is characterized by very different climates depending upon the subregion. The Moroccan climate is influenced by the Atlantic Ocean to the west, the Mediterranean Sea to the north, the dry Saharan air to the south and is locally modulated by the orographic effects induced by the Atlas Mountains (see Fig. 1). These factors have a strong impact on the variability of moisture and other surface weather parameters (Knippertz et al.,2003).

    Fig. 1. Map showing orography in meters and the position of the synoptic meteorological stations used in this study, mainly located in airports, and being operational along the whole day (24 h).

    3. Methods

    3.1 Analog ensemble as prediction system

    As shown in Fig. 2, the basic idea behind analog ensemble method is to find synonymous weather situations to the current one. To achieve this goal, we use the past forecasts database provided by a deterministic NWP model, which form analogs dataset (step 1 in Fig. 2), and a time series of analogs verifying observations, which will be ensemble members (step 2 in Fig. 2), over a given location. Taken all together, these observations constitute the ensemble prediction for the current forecast (step 3 in Fig. 2). Then, the deterministic prediction value of the predictand is the mean of the ensemble.A weather situation may extend from a few hours to a few days(weather regimes predominate the weather situation for several days). Ordinarily the forecast period of analog method is very short, often up to 6 h (Horton et al.,2017), 12 h (Riordan and Hansen, 2002), and rarely 24 h(Hansen, 2007), since analog methods often work correctly on homogeneous weather conditions which is elementary for this type of forecast. The dataset of past forecasts imperatively contains a set of meteorological variables used as predictors.

    Fig. 2. Analog ensemble algorithm steps with day neighborhood and search restrictions.

    Fig. 3. (a) Dispersion diagram for 10-m wind speed (random forest) and (b) 2-m temperature (linear regression). The solid line is the average ensemble spread and the dotted/dashed lines are the root-mean-square error (RMSE) of the ensemble mean. Four configurations are shown:DM13 with all predictors weights equal to one (DM13), DM13 with weights issued from machine learning technique (RF/LR + DM13), DM13 with daily neighborhood (DM13 with restriction), and DM13 with weights issued from machine learning technique and daily neighborhood(RF/LR with restriction).

    For a given location and time, one seeks to predict a variable from the set named as predictand, and uses all the eight available variables as predictors including the predictand. For example, naming the temperature at 2 m as predictand, the predictors are: T2m, RH2m, MSLP,SURFP, WS10m, WD10m, ZW10m, and MW10m from the past forecast dataset.

    In order to select the potential analogs, the basic similarity metric of Delle Monache et al. (2013) has been modified and used in this study. The basic DM13 metric is defined as follows:

    wheretis the current NWP deterministic forecast valid at the future timetat a station location;At′ is an analog(past forecast) at the same location and with the same forecast lead time but valid at a past timet′;Nνandwiare the number of physical variables used in the analogs search and their weights, respectively; σiis the standard deviation of the time series of past forecasts of a given variableiat the same location and forecast lead time;t? is equal to half the number of additional times over which the metric is computed; andFi,t+jandAi,t′+jare the values of the forecast and the analog in a time window withfor a given variablei.

    To assess the impact of the day neighboring on the AnEn performance, this criteria is added to DM13 formula in order to capture flow dependency and to force analogs to be in the same season and at a near date to the future forecast (target). Thus, Eq. (2) used in the modified version of DM13 is defined as follows:where subscriptstandt′ represent the lead times of a forecast in the future (F) and in the past (A, i.e., a potential analog) respectively. The subscriptis equal to half temporal window of additional times over which the distance is calculated; andFi,t+jandAi,k,t′+jare the values of the forecast and the analog in a time window of 2 ×h and 2 ×days for a given variablei, whereis the day window for the daily neighborhood configuration andk∈[target_date ?target_date +].

    Analog search dataset (training) is restricted only to days within the day neighborhood in previous years. To retrieve the basic formula of DM13 similarity metric where AnEn looks for analogs throughout the available historical dataset, it suffices to drop daily neighborhood indexkfrom Eq. (2).

    3.2 Weight optimization strategies

    In contrast with the DM13 method, which uses few predictors and where predictors weights are all equal and initiated to 1, hereinstead, for every predictand from the observed features, the set of predictors is constructed from all available eight parameters. In addition, a machine learning based approach is used as a weighting strategy. Thus, three machine learning models are constructed (Table 1), tuned, and cross validated all over the training period to find the optimal weights used as input to AnEn.

    Table 1. Brief description of the machine learning techniques used for computing predictors weights

    The best model found for each technique is used to find a generic equation that links the predictand to predictors (called also features), and then predictors importance’s coefficients are inferred. The importance coefficients are then scaled and normalized to form predictors weights in AnEn. Neural networks technique was excluded from the benchmark due to the hardness in physical interpretation of importance coefficients in output.Features importance is variously calculated from a machine learning techniques family to another. The minimization method is a centerpiece in the process. The ordinary least squares method is usually used to find the weights that minimize the squared differences between the actual and the estimated outcomes as in Eq. (3).

    wherey(i)is theith observation for the predictandy,refers to theith value for predictorxj, and αjis the linear coefficient of predictorxj. For linear regression models predictors importance can be measured by the absolute value of itst-statistic. Thet-statisticis the estimated weightscaled with its standard error SEas shown in Eq. (4).

    For the decision tree based methods, feature import-ance is defined as the decrease in node impurity times the probability of reaching that node. The node probability equals the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the variable. At each split in each tree the improvement in the split criterion—here is mean square error—is the importance measure attributed to the splitting variable and is accumulated over all the trees in the trees ensemble (forest) separately for each variable.

    3.3 Experimental design

    For each meteorological parameter as a target predictand (T2m, RH2m, MSLP, SURFP, WS10m, WD10m,ZW10m, and MW10m), two configurations of daily neighboring are the basic DM13 version where analogs are selected from any date regardless of their season or month; and the modified version proposed in this study,where we select analogs imperatively from a day window around the day of the target. By this manner, we make sure that analogs belong to the same season as the target. This aims to assess the flow dependency of the AnEn performance, thus gaining time for operational use when looking for the best analogs.

    For each configuration, a weight optimization of the eight AnEn predictors was performed over all studied airports using the three techniques (XGBoost, random forest, and linear regression), for the 1–24 forecast hours over the training period (2016–2018). For all airports, the combination leading to the lowest mean square error of the machine learning (ML)-developed model is chosen and kept constant over the testing period (2019). The set of eight predictors were used because they are considered relevant for the prediction of the target variable and are the main available parameters in SYNOP messages. This gives a potential for this to be used worldwide. Additional variables, which have not been tested in this study, could further improve the results. The DM13 basic weighting strategy is also performed and used as a benchmark.

    As a result, 64 configurations (8 parameters × 2 neighborhood configurations × 4 weight optimization strategies) have been performed over all the 15 airports using the hourly AROME forecasts at the nearest grid point and the equivalent observations (Table 2). Each configuration provides a 25-member analog ensemble forecast.Allthepossiblecombinationsare defined with theconstraintwherewiisthe predictors weights. For the second category of experiments, the same configurations have been carried out, over the training period 2016–2019, to assess the impact of the boundary condition change on the AnEn performance over the testing period (2020).

    Table 2. Experimental setup with more details about the used configurations

    4. Results

    In this section, we analyze first the weight optimization results. Then, the AnEn’s performance with the different configurations described previously is assessed and compared to the original version of DM13 using common verification metrics for evaluation of deterministic predictions, namely Bias (mean error) and RMSE.These metrics are described with more details in Jolliffe and Stephenson (2003) and Wilks (2005). However, as an ensemble forecast, AnEn prediction system must be statistically consistent with the observations in the large scale flow given the study domain is large. This statistical consistency is assessed in this study using the spread-error diagram.

    4.1 Weight optimization results analysis

    Many research studies on AnEn assign equal weightswi=1to each predictor (Delle Monache et al., 2013;Delle Monache, 2015). To take into account the strength of the relationship between individual predictors and the target variable, the predictors weight optimization has been performed based on three machine learning techniques (XGBoost, random forest, and linear regression).

    Table 3 shows the percentage of occurrence of all possible weight values over all airports for each predictor,where T2m is taken as predictand. It is seen clearly from this table that the past forecasted T2m is the more relevant predictor, as expected, followed by MSLP and SURFP with its weights taking values greater than 0 at 48.4% of the nearest grid points from the studied airports.

    Table 3. Percentage of occurrence of possible weights values for each predictor for T2m as predictand

    Results analysis for the other variables shows the relevance of MSLP and SURFP as predictors for all of them indicating the influence of large scale atmospheric conditions on each predictor. In addition, it is found that the zonal wind component also takes an important weight for RH2m as predictand. In fact, RH2m is influenced by the zonal circulation of air-masses and atmospheric flows.For zonal and meridional 10-m wind components, it is also found that 10-m wind speed is a relevant predictor.This is obvious by the nature of construction of this predictor, which is equal to the root square of the sum of wind components squares. While each variable is highly correlated with itself as predictor, some discrepancies are found between the three machine learning techniques regarding the order of high weights affectation to predictors.

    Applying this static weighting strategy makes it possible to select the potential AnEn predictors for probabilistic target variable forecasting and also to find their optimal weights. This could improve the AnEn performance since their selection is based on physical link between predictors and target variables.

    4.2 Statistical consistency of analog ensemble

    An especially important aspect of ensemble forecasting is its capacity to yield information about the magnitude and nature of the uncertainty in a forecast. While dispersion describes the climatological range of an ensemble forecast system relative to the range of the observed outcomes, ensemble spread is used to describe the range of outcomes in a specific forecast from an ensemble system. Qualitatively, we have more confidence that the ensemble mean is close to the eventual state of the atmosphere if the dispersion of the ensemble is small.Conversely, when the ensemble members are very different from each other the future state of the atmosphere may be more uncertain (Wilks, 2005).

    If on any given forecast occasion, the observationois statistically indistinguishable from any of the ensemble membersmi, then clearly the bias is zero sinceE[mi]=E[oi]. Statistical equivalence of any ensemble member and the observation further implies that

    where em denotes the ensemble mean. By applying the root square to both sides of Eq. (5), it is easy to see that the left-hand side is the RMSE for the ensemble-mean forecasts, whereas the right-hand side expresses dispersion of the ensemble membersmiaround the ensemble mean (Wilks, 2005). It is important to realize that Eq. (5)holds only for forecasts from a consistent ensemble, and in particular assumes unbiasedness in the forecasts.

    Two consequences of the ensemble consistency condition are that forecasts from the individual ensemble members (and therefore also the ensemble-mean forecasts) are unbiased, and that the average (over multiple forecast occasions) MSE for the ensemble-mean forecasts should be equal to the average ensemble variance(Wilks, 2005).

    Figure 3 displays the dispersion diagram for 10-m wind speed and 2-m temperature. In this diagram, we have plotted the evolution of the average ensemble spread and RMSE of the ensemble mean as function of lead-time, over all the stations for both parameters. This was done for four configurations: (1) basic DM13 version, (2) DM13 with weights issued from machine learning technique and no restriction on analogs dates, (3)DM13 with daily neighborhood restriction, and (4) AnEn with daily neighborhood restriction and weights issued from machine learning technique.

    For 10-m wind speed, DM13 with random forest weights and DM13 basic configurations seem to be the most statistically consistent since the spread is close enough to RMSE with the minimum RMSE linked to DM13 with random forest configuration. For T2m,DM13 with daily neighborhood restriction is the bestconfiguration in terms of statistical consistency (differences magnitude not exceeding 0.25°C) followed by AnEn configuration with daily neighborhood restriction and linear regression weights.

    For the other parameters, the most statistically consistent configurations are DM13 with XGBoost weights for RH2m (difference below 1%), DM13 with random forest weights for MSLP and SURFP (difference below 0.5 hPa),and DM13 with weights from XGBoost for WD10m (not shown).

    For most parameters, it is found that the good statistical consistency of the AnEn comes from a lower RMSE of the ensemble mean, which is achieved from use of higher-resolution NWP AROME model. In addition, a lower ensemble spread also contributes to the consistency of the forecast system.

    Overall, it is found that configurations with weights issued from machine learning techniques without applying the daily neighborhood restriction are almost the most statistically consistent with the lowest RMSE. A more indepth assessment of a statistical consistency at a particular season or forecast lead time will be discussed later.

    4.3 Deterministic verification over the study domain

    4.3.1Global performance as function of lead time

    In this section, the AnEn performance with the best configurations, trained during 2016–2018, is assessed and compared for each of the studied surface weather parameters over 2019, firstly to the operational NWP AROME model outputs and secondly to the AnEn configuration of DM13 where equal weights (= 1) are assigned to predictors and no daily neighborhood restriction is applied. To achieve this, Fig. 4 displays the temporal evolution of the Bias (mean error) and RMSE as function of lead time for T2m, RH2m, MSLP, SURFP,WD10, and WS10. The plotted metrics are the average value over all the 15 airports.

    From Fig. 4, the performance of the operational model AROME (black line) shows a high variability, depending on the studied surface weather parameter and lead time. For instance, AROME predicts RH2m with moist bias not exceeding 3% associated with an RMSE of 10%.While AROME shows a cold bias of about ?0.5°C and an RMSE of 1.5°C for T2m. For WS10m and WD10m,AROME Bias is below 0.25 m s?1and 3 degrees respectively. For barometric fields, both MSLP and SURFP have a bias below 1 hPa and an RMSE of 2 hPa. These scores are similar to the findings of Seity et al. (2011).

    Fig. 4. Bias and RMSE curves for continuous prediction of the following surface parameters (T2m, RH2m, MSLP, SURFP, WS10m, and WD10m) using the best daily neighborhood restriction and machine learning weighting configuration (presented in dotted blue line), NWP AROME prediction of those parameters are plotted in black solid line, and finally the basic DM13 method is plotted in triangular red line.

    Comparing the performances of the basic DM13 and those of the modified version configurations, the latter displays significant improvement of RMSE compared to DM13 for MSLP, SURFP, and T2m, while DM13 has the best RMSE for RH2m. Regarding the Bias, it is found that the basic DM13 has the best score for T2m,MSLP, and SURFP. The modified version shows better Bias only for RH2m; however, both methods show similar Bias values for WD10m and WS10m, where they outperform slightly each other for different lead times.

    Comparing AnEn configurations and AROME performances, it is found that AnEn outperforms significantly AROME in terms of Bias during the night and early morning (lead times from 0 to 10 h and from 18 to 23 h)for all parameters. Regarding the RMSE, the best scores of AnEn are found for WS10m and WD10m.

    During the nighttime forecast hours, a reduction varying between 7% and 20% of AROME’s error for WS10m, SURFP, and WD10m is perceived. These improvements in RMSE can be explained by the fact that AnEn ensemble members are constructed mainly from real observations that in 60% cases belong to the daily neighborhood (not shown), which preserves the variation in RMSE magnitude. In addition, machine learning AnEn’s configurations take into account only significant predictors. This avoids using irrelevant predictors that add more error and inaccuracy to analogs selection process. Feature selection techniques often reduce the RMSE of regression coefficients, in particular for weak or noisy predictors (Heinze et al., 2018). However, a slight degradation of the RMSE is found for the thermodynamic surface parameters (RH2m and T2m) in comparison with AROME (Fig. 4). This could be due to the high spatiotemporal variability of these parameters and also to the model forecast error. This might indicate that AnEn needs more predictors representing the lower layer of the atmospheric boundary layer instead of using only the main surface weather parameters issued from the SYNOP messages.

    From a seasonal dependency point of view, it is found that the modified version AnEn DM13 yield improvement of Bias and RMSE in winter and spring during the daytime forecast hours (from 10 to 18 h) while slight worsenings are found for both metrics in summer and autumn (not shown). This is mainly due to the limited ability of AROME in predicting the rapid convective mesoscale situations during these months (Seity et al., 2011).In the next section, the spatial distribution of AnEn performance is investigated to assess any dependency of region and season.

    4.3.2Global performance as function of spatial location

    To assess the global performance of AnEn over the study domain, the Bias and RMSE gain/loss [Eq. (6)], in comparison with AROME, have been calculated for each airport over all the lead times and are plotted spatially(RH2m as an example in Fig. 5) for DM13 and the best configuration of the modified version of DM13.

    where Scoreanenand Scorenwpare relatively the scores for AnEn and NWP.

    Indeed, when the NWP score is perfect (= 0), this score is converted to 0.01 to avoid infinite loss value.

    For 2-m relative humidity (Fig. 5), AROME model draws a positive bias over coastal areas (up to 10%), and negative bias for two airports in the northeastern part,while the RH2m bias is very weak in the other airports.With regard to AROME, AnEn yielded an important improvement by reducing RH2m Bias by up to 50% for almost all the airports while it reduced the RH2m RMSE by about 25%. It should be noted that AnEn degrades the AROME Bias in six airports in the north part of Morocco; most of these stations already have an almost perfect Bias by AROME.

    Fig. 5. Bias and RMSE gain of RH2m at each airport for DM13 and AnEn’s best configuration. The same metrics (Bias and RMSE) for the operational AROME NWP model at each station are also plotted as a benchmark.

    Similarly, the spatial AnEn performance analysis for the other surface parameters points out that AROME shows a negative Bias for T2m especially over coastal areas (not shown), while AnEn highlights an improvement up to 50% in Bias and 25% in RMSE for most of the airports from different regions (Mediterranean, Atlantic coastal areas, southern region, interior, and eastern region). For the rest of airports, AnEn converts negative Bias to positive Bias with magnitude not exceeding 1.5°C.

    Besides, AROME highlights a negative bias for mean sea level pressure and surface pressure all over Mediterranean and Atlantic coastal areas and a weak bias far inland. AnEn reduced pressure bias and RMSE magnitude concurrently by 50% for most airports, except a few interior airports where AnEn degrades these metrics. AnEn best configuration reduced Bias and RMSE of wind speed by more than 40% of its magnitude, this finding is generalized for all airports.

    For wind direction, AROME underlines very weak Bias in general except for few interior airports where Bias exceeds 9 degrees, while AnEn reduced Bias magnitude in those areas to 2.5 degree or by 50% and preserved slightly bias nullity all over the rest. The RMSE of wind direction was reduced by AnEn up to 60%. For wind components, AROME represents a negative Bias over all airports (range from ?20 to ?5 m s?1), AnEn lower Bias magnitude by 70%. In addition, RMSE error was reduced by 75% for 10-m wind components.

    In Figs. 6 and 7, we have plotted the seasonal spatial distribution of gain/loss in Bias and RMSE for T2m, using DM13 random forest configuration. It is seen clearly that AnEn performances are seasonally dependent. Indeed, Bias gain exceeds 50% for most of the airports especially in autumn and summer; while a general worsening is found in spring, one can remark that Bias gain reached 70% for some locations on the Atlantic side. Regarding the RMSE, seasonal gain for T2m is around 20% for at least half of the studied airports except for spring.

    Overall, the gain/loss behavior for Bias and RMSE is different from one parameter to another and is highly dependent on season and location. In this context, Bias gain exceeds 50% for 70% of parameters (not shown). Slight degradations are observed in the airports with good seasonal Bias.

    A limitation of AnEn is a slight degradation of seasonal RMSE for some tested configurations and parameters(not shown). This RMSE loss is mainly observed in the Mediterranean and eastern airports (Tanger, Al Hoceima,and Oujda). It is also found that neighborhood restrictions showed valuable improvements for some parameters (T2m, RH2m, WS10m) and contributed to increase the RMSE and Bias gain for different locations and seasons for those parameters.

    4.4 Sensitivity to boundary conditions

    As mentioned before, AnEn leverages a single deterministic NWP model to produce probabilistic forecasts.Thus, it is highly recommended to use the same model.In our case, only the lateral boundary conditions have been changed in 2020.

    First, the impact of this change on the AROME performance itself is assessed. It is found that the new boundary condition coupling used in 2020 softly impacted AROME’s performance for most of the parameters (10-m wind speed is shown as an example in Fig. 8).Indeed, improvements in RMSE (not shown) and Bias are noticeable mainly for synoptic dynamical parameters such as MSLP, SURFP, WS10m, and 10-m wind components. On the other hand, AROME’s performances are of no significant change for RH2m, T2m, and WD10m.

    Fig. 6. Bias gain/loss for T2m for all seasons using no daily neighborhood restriction and random forest weights configuration.

    Fig. 7. RMSE gain/loss for T2m for all seasons using no daily neighborhood restriction and random forest weights configuration.

    Fig. 8. Wind speed Bias over 2020 for all lead times and all the possible AnEn configurations.

    However, AnEn performance was not affected by the boundary conditions change. Indeed AnEn still outperforms AROME spatially, temporally, and seasonally for most of the surface parameters and locations. AnEn still shows best Bias and RMSE during nights and mornings over 2020 for most of the surface parameters. A finding to underline is that AnEn Bias gets improved during day lead times where it overtakes AROME for all parameters except MSLP and SURFP. On the other hand, AnEn RMSE remains the best overall. It is to mention that AnEn best configurations of 2019 are not preserved for 2020. For instance for WS10m, the best AnEn configuration during 2019 was DM13 + RF while it is DM13 +XGB for 2020.

    5. Discussion

    In this study, it is demonstrated that the best configurations with multiple criteria of AnEn yield important improvement in surface weather parameters forecasting but it still has some shortcomings.

    Indeed, the perfect analogy is far from existing, but identifying close enough situations leading to similar effects is still possible. Then, the relevance of analogy and thus analogs forecasting quality is tightly affected by the three major following factors: i) the process of skillful analogs selection in the training data, which depend on the similarity metric, predictors weighting and selection,temporal window around the target lead time, and the number of members; ii) the target surface weather parameter and its predictability by data-driven forecasting techniques; and iii) the used NWP model error in the training data and its ability to forecast rare events and some mesoscale phenomena (Zhao and Giannakis, 2016).

    In this research, we tackled the main surface weather parameters issued from SYNOP messages. While AnEn improves the synoptic scale parameter forecasting(MSLP, SURFP, WS10, and WD10) by reducing both Bias and RMSE, it is found that AnEn improves slightly the Bias but degrades the RMSE for the thermodynamic surface parameters (RH2m and T2m) during the daytime lead times and also spatially in some airports. This could be overcome by adding predictors representing the lower layer of the atmospheric boundary layer in addition to parameters that describe the atmospheric circulation such as geopotential fields in many levels (Duband, 1981;Guilbaud, 1997) or new sets of predictors at different pressure levels (Z500, TPW850, etc.) (Horton et al.,2012). In fact, even for two locations that are close to each other but subject to different critical atmospheric conditions, the selection of the best predictors can vary.Thus, the method needs to be adapted to local conditions,available data, and the size of the region of interest. In this framework, three machine learning techniques (linear regression, random forest, and extreme gradient boosting) have been used in this study to find the optimal weights for the physically meaningful predictors. This step of AnEn forecasting process aims to maximize the useful information and reduce noise. The optimal importance of predictors however varies from region to another,a season to another, and along with the leading atmospheric process. Indeed, Junk et al. (2015) stated that optimized predictor weights are highly affected by terrain complexity and atmospheric stratification. In our case,using machine learning technique to optimize the predictor weights leads to an improvement in Bias and RMSE exceeding 50% while the gain reached 21% in Gensler et al. (2016), 20% in Junk et al. (2015), and 44% in Wang et al. (2019).

    One key aspect in the analogy is the seasonal preselection of the analogs. This preselection is implemented in this study as a moving selection of ±15 days centered around the target date for every year of the archive and time window of ±1 h around the forecast hour since the hourly forecasts are used here and are issued from a high-resolution mesoscale operational model. However,it is found that seasonal preselection yields worsening of AnEn performance in some airports. Indeed, for one target day, the sampling (31 days/yr × 3 yr of training = 93 days) might be inadequate to retrieve the skillful analogs due to the missing observed values or to the occurrence of rare events in this temporal window. Thus, it is concluded that this approach requires a very long archive that is why no restriction on target date neighboring performs better. Indeed, an analysis of the selected analogs position from the target has been performed and it is found that for some cases, many analogs get outside the daily neighborhood window (±15 days). Hence, it would be very beneficial to extend that window. This is in line with the finding of many previous studies for climate purpose that uses daily neighborhood criterion to detect relevant analogs (Bontron, 2004; Horton et al., 2012; Ben Daoud et al., 2016). In ensemble forecasting, the number of members is a parameter of higher relevance. Hence,finding the optimal number of members (analogs) that improves AnEn performances is highly demanded (Horton, 2019; Li et al., 2020). Furthermore, beyond topography, a more precise distinction between spatial areas(urban and non urban for example) can enhance AnEn performance interpretation and point out the ability of using such methods in highly dense urban cities (Li et al.,2020). In addition, some research studies used a large forecast range to investigate the potential of AnEn to capture well the diurnal cycle of the weather surface parameters such as T2m and WS10m (Wang et al.,2019).

    6. Conclusions

    This study presents a new application of the analog ensemble method to improve surface weather parameters prediction over 15 airports of Morocco during 5-yr period (2016–2020), from the non-hydrostatic mesoscale operational model AROME hourly forecasts and observations issued of SYNOP messages. The analog ensemble method application is extended for the first time to include eight main weather surface parameters (T2m,RH2m, MSLP, SURFP, WS10m, WD10m, ZW10m, and MW10m). Seasonal impact was considered in the analogs search process by applying daily neighborhood restriction, in a way that all the analogs have the same season as the current forecast. Best analogs for AnEn are searched by using the studied parameters as predictors,given optimized weights that are calculated as a normalized feature importance coefficients issued from machine learning techniques (linear regression, random forest, and XGBoost) over the training period(2015–2018). Verification of the performance of AnEn was carried out mainly over 2019. An additional verification over 2020 also was held to assess AnEn sensitivity to lateral boundary conditions change.

    The results from the spatial and temporal scores analysis showed that AnEn best configurations produce notably lower Bias and RMSE compared to AROME during night lead times, especially for large scale synoptic parameters (SURFP, MSLP, WD10m, and WS10m).However during day lead times, AnEn shows some limitations, in particular for thermodynamic surface parameters (T2m and RH2m). This is mainly due to the high spatiotemporal variability of these parameters and also to the high RMSE of the ensemble mean and also to higher ensemble spread in some cases during daytime hours. Similar results are reached by AnEn also for most airports,indicated by spatially averaged Bias and RMSE reduction up to 50% and 30% depending on the season. Despite the advantage of lower computational cost, seasonal preselection yields a performance degradation in some airports almost due to the weakness of sampling adequacy.

    According to World Meteorological Organization(WMO), the wind speed value is rounded to the nearest integer in the SYNOP messages. Then, the accuracy of the wind measurement is 1 m s?1. Similarly, the wind direction is coded in a wind rose with 36 directions. This induces an uncertainty of about 10 degrees. The 2-m relative humidity is also rounded to the nearest integer. Consequently, this leads to an accuracy of 1%. All these observation error sources impact the assessment of the AnEn performance while using the continuous verification scores such as Bias and RMSE. One way to overcome these limitations is using the precise observed values from the automatic weather stations that have become an increasingly prominent part of meteorological observation networks over the last 20–30 years and most or all synoptic observations are now automated in some countries.

    The results reported herein can be further improved with a longer training dataset, by extending existing training datasets to consider neighboring locations while searching analogs, exploring further similarity metrics,and by adding more predictors from lower layers of the atmospheric boundary layer or parameters that describe the atmospheric circulation predictors.

    Acknowledgments.We would like to thank reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.

    91成年电影在线观看| 美女扒开内裤让男人捅视频| √禁漫天堂资源中文www| 一级毛片精品| 亚洲精品中文字幕在线视频| 亚洲片人在线观看| 69av精品久久久久久| 男人操女人黄网站| 成熟少妇高潮喷水视频| 亚洲avbb在线观看| 久久久久免费精品人妻一区二区 | 色在线成人网| 极品教师在线免费播放| 久久久精品国产亚洲av高清涩受| 国产aⅴ精品一区二区三区波| 两性午夜刺激爽爽歪歪视频在线观看 | 国产精品98久久久久久宅男小说| 国产男靠女视频免费网站| 国产成年人精品一区二区| 亚洲一码二码三码区别大吗| 很黄的视频免费| 窝窝影院91人妻| 999久久久精品免费观看国产| 国产又色又爽无遮挡免费看| 成人亚洲精品一区在线观看| 给我免费播放毛片高清在线观看| www.精华液| 亚洲va日本ⅴa欧美va伊人久久| 少妇粗大呻吟视频| 一个人免费在线观看的高清视频| 制服诱惑二区| 免费一级毛片在线播放高清视频| 精品国产超薄肉色丝袜足j| 中文字幕人成人乱码亚洲影| 国产一区二区激情短视频| 欧美日韩一级在线毛片| 国产99久久九九免费精品| 国产精品99久久99久久久不卡| 99国产极品粉嫩在线观看| 亚洲精品国产精品久久久不卡| 日韩高清综合在线| 国产av一区在线观看免费| 精品卡一卡二卡四卡免费| 亚洲国产高清在线一区二区三 | 国内精品久久久久久久电影| 日日夜夜操网爽| 亚洲一区二区三区色噜噜| 久久狼人影院| 久久精品成人免费网站| 正在播放国产对白刺激| 夜夜看夜夜爽夜夜摸| 久久亚洲精品不卡| 午夜福利视频1000在线观看| 高清在线国产一区| 免费在线观看影片大全网站| 免费在线观看完整版高清| 亚洲黑人精品在线| 国产成人欧美在线观看| 午夜久久久久精精品| 在线国产一区二区在线| 久热爱精品视频在线9| 国产精品久久久久久亚洲av鲁大| 少妇被粗大的猛进出69影院| 国产精品自产拍在线观看55亚洲| 亚洲五月婷婷丁香| 欧美丝袜亚洲另类 | 亚洲国产精品999在线| 久热这里只有精品99| 国产黄a三级三级三级人| 中文字幕人妻丝袜一区二区| 国产精品久久久人人做人人爽| 一区福利在线观看| 美女高潮到喷水免费观看| 露出奶头的视频| 色播亚洲综合网| 午夜激情福利司机影院| 老司机福利观看| 看片在线看免费视频| 免费在线观看视频国产中文字幕亚洲| 动漫黄色视频在线观看| 午夜免费成人在线视频| 在线观看一区二区三区| 99精品欧美一区二区三区四区| 俄罗斯特黄特色一大片| a级毛片在线看网站| 久久久国产成人免费| 99久久综合精品五月天人人| 一级毛片高清免费大全| 亚洲国产欧美日韩在线播放| 变态另类丝袜制服| 少妇的丰满在线观看| 美国免费a级毛片| 日韩成人在线观看一区二区三区| 少妇裸体淫交视频免费看高清 | 欧美另类亚洲清纯唯美| 亚洲午夜精品一区,二区,三区| www日本黄色视频网| 免费看美女性在线毛片视频| 午夜老司机福利片| 欧美不卡视频在线免费观看 | 国产成人精品久久二区二区91| 夜夜夜夜夜久久久久| 在线国产一区二区在线| 一级a爱片免费观看的视频| 91av网站免费观看| 国产片内射在线| 亚洲成人免费电影在线观看| 中文字幕人妻丝袜一区二区| 亚洲一卡2卡3卡4卡5卡精品中文| 精品国内亚洲2022精品成人| 亚洲专区中文字幕在线| 久久中文字幕人妻熟女| 欧美日韩亚洲国产一区二区在线观看| 国产精品亚洲一级av第二区| 日本精品一区二区三区蜜桃| 久久香蕉激情| 两性午夜刺激爽爽歪歪视频在线观看 | 草草在线视频免费看| 久久亚洲真实| 国产精品电影一区二区三区| 国产高清视频在线播放一区| 久久久久久大精品| 日本三级黄在线观看| 国产精品99久久99久久久不卡| 国产av一区二区精品久久| 亚洲五月天丁香| 精品第一国产精品| 午夜影院日韩av| 亚洲一码二码三码区别大吗| 日本在线视频免费播放| 老汉色∧v一级毛片| 亚洲国产欧洲综合997久久, | 久久久国产欧美日韩av| 最近最新免费中文字幕在线| 正在播放国产对白刺激| 大型黄色视频在线免费观看| 欧美日韩中文字幕国产精品一区二区三区| 欧美成人免费av一区二区三区| 曰老女人黄片| 久久久久久久久久黄片| 美女 人体艺术 gogo| 国产亚洲精品综合一区在线观看 | 欧美日韩乱码在线| 人人澡人人妻人| 999久久久国产精品视频| 90打野战视频偷拍视频| 亚洲欧美精品综合久久99| 手机成人av网站| 免费电影在线观看免费观看| 久久午夜亚洲精品久久| 国产亚洲精品综合一区在线观看 | 亚洲av日韩精品久久久久久密| 18禁黄网站禁片午夜丰满| 色播在线永久视频| 18禁国产床啪视频网站| 女警被强在线播放| 中文字幕高清在线视频| 午夜福利欧美成人| 午夜福利视频1000在线观看| 波多野结衣巨乳人妻| 亚洲真实伦在线观看| 露出奶头的视频| 亚洲人成伊人成综合网2020| 色综合欧美亚洲国产小说| 欧美最黄视频在线播放免费| 不卡av一区二区三区| 国产爱豆传媒在线观看 | 女性生殖器流出的白浆| 18禁裸乳无遮挡免费网站照片 | 亚洲人成网站在线播放欧美日韩| 最好的美女福利视频网| 女性生殖器流出的白浆| 久久午夜亚洲精品久久| www.熟女人妻精品国产| 成人永久免费在线观看视频| 日韩欧美 国产精品| 免费av毛片视频| 欧美黑人巨大hd| 亚洲av电影不卡..在线观看| 无限看片的www在线观看| 日本五十路高清| 精品久久久久久久毛片微露脸| 久久精品91无色码中文字幕| 免费在线观看亚洲国产| 一本精品99久久精品77| 一本综合久久免费| 91字幕亚洲| 啦啦啦 在线观看视频| 久久精品成人免费网站| 亚洲第一欧美日韩一区二区三区| 一级毛片精品| 欧美一区二区精品小视频在线| 亚洲激情在线av| 日韩大码丰满熟妇| 观看免费一级毛片| 一区二区三区激情视频| 欧美黑人精品巨大| av超薄肉色丝袜交足视频| 欧美丝袜亚洲另类 | 长腿黑丝高跟| 欧美日韩一级在线毛片| 老司机在亚洲福利影院| 欧美性猛交黑人性爽| 91麻豆av在线| 此物有八面人人有两片| 久久精品成人免费网站| 我的亚洲天堂| www.自偷自拍.com| 99热只有精品国产| 国产亚洲av高清不卡| 12—13女人毛片做爰片一| 无遮挡黄片免费观看| 久热这里只有精品99| 女性生殖器流出的白浆| 国产色视频综合| 黄片播放在线免费| 999久久久精品免费观看国产| 日韩欧美三级三区| 久久午夜亚洲精品久久| 亚洲天堂国产精品一区在线| 国产精品国产高清国产av| 国产三级在线视频| 精品国产乱码久久久久久男人| 桃色一区二区三区在线观看| 在线国产一区二区在线| 97碰自拍视频| 日本免费a在线| 亚洲avbb在线观看| 精品不卡国产一区二区三区| 女生性感内裤真人,穿戴方法视频| 国产成人欧美在线观看| 一进一出抽搐gif免费好疼| 性色av乱码一区二区三区2| 国产片内射在线| 免费看a级黄色片| 视频在线观看一区二区三区| www.熟女人妻精品国产| 热re99久久国产66热| 成人亚洲精品av一区二区| 9191精品国产免费久久| 99国产极品粉嫩在线观看| 国产一区二区三区在线臀色熟女| 午夜老司机福利片| 中文字幕精品免费在线观看视频| 在线视频色国产色| 亚洲aⅴ乱码一区二区在线播放 | 99热6这里只有精品| 中文字幕高清在线视频| www.自偷自拍.com| 欧美成人性av电影在线观看| 久久香蕉激情| 久99久视频精品免费| 国内久久婷婷六月综合欲色啪| 老熟妇乱子伦视频在线观看| 啦啦啦免费观看视频1| 欧美日韩乱码在线| 国产成人啪精品午夜网站| 欧美精品亚洲一区二区| 午夜福利一区二区在线看| av超薄肉色丝袜交足视频| svipshipincom国产片| 亚洲精品一卡2卡三卡4卡5卡| 国产av一区二区精品久久| 国产精品 国内视频| 亚洲国产中文字幕在线视频| 欧美不卡视频在线免费观看 | 在线观看一区二区三区| 久久亚洲真实| 久久久久久久久免费视频了| 亚洲 欧美 日韩 在线 免费| 亚洲第一青青草原| 欧美日韩一级在线毛片| 99国产精品99久久久久| 婷婷精品国产亚洲av在线| 亚洲性夜色夜夜综合| 欧美黄色淫秽网站| 成年版毛片免费区| 在线观看一区二区三区| 男女之事视频高清在线观看| 成年女人毛片免费观看观看9| 99国产精品一区二区三区| 精华霜和精华液先用哪个| 成人午夜高清在线视频 | 久久中文看片网| 一进一出抽搐动态| 国产精品野战在线观看| 日韩免费av在线播放| 19禁男女啪啪无遮挡网站| 一进一出抽搐动态| 熟妇人妻久久中文字幕3abv| 搡老岳熟女国产| 午夜免费激情av| 日日摸夜夜添夜夜添小说| 韩国精品一区二区三区| 露出奶头的视频| 人妻久久中文字幕网| 手机成人av网站| 国产精品二区激情视频| 久久久久久九九精品二区国产 | 亚洲狠狠婷婷综合久久图片| 黑人欧美特级aaaaaa片| 黄色女人牲交| 在线播放国产精品三级| 搡老岳熟女国产| xxxwww97欧美| 久久精品人妻少妇| bbb黄色大片| 亚洲一区二区三区色噜噜| 欧美在线一区亚洲| 欧美激情久久久久久爽电影| 精品久久久久久久毛片微露脸| 久久伊人香网站| 亚洲国产欧美日韩在线播放| 美女午夜性视频免费| 久久国产精品影院| 他把我摸到了高潮在线观看| 99国产综合亚洲精品| tocl精华| 国产亚洲精品av在线| 2021天堂中文幕一二区在线观 | 50天的宝宝边吃奶边哭怎么回事| 国产精品综合久久久久久久免费| 午夜福利免费观看在线| 十分钟在线观看高清视频www| 激情在线观看视频在线高清| 少妇被粗大的猛进出69影院| 国产精华一区二区三区| 一区二区三区国产精品乱码| 亚洲成人精品中文字幕电影| 国产成人精品久久二区二区91| 最新美女视频免费是黄的| 男人舔女人的私密视频| 少妇裸体淫交视频免费看高清 | x7x7x7水蜜桃| 777久久人妻少妇嫩草av网站| 国产精品,欧美在线| 免费在线观看黄色视频的| 一本大道久久a久久精品| 一进一出好大好爽视频| 可以在线观看的亚洲视频| 老司机深夜福利视频在线观看| 亚洲精品粉嫩美女一区| 久久久久久久久免费视频了| 香蕉av资源在线| svipshipincom国产片| 国产在线观看jvid| 成年女人毛片免费观看观看9| 久久精品人妻少妇| 日韩一卡2卡3卡4卡2021年| 国产成人欧美| 18禁黄网站禁片午夜丰满| 国产午夜福利久久久久久| 国产精品 国内视频| 国产免费男女视频| 亚洲欧美一区二区三区黑人| 亚洲国产日韩欧美精品在线观看 | 中文字幕精品免费在线观看视频| 女生性感内裤真人,穿戴方法视频| 久久久久国产一级毛片高清牌| 欧美一级a爱片免费观看看 | 国产精品 国内视频| 久久久久精品国产欧美久久久| 久久久久久免费高清国产稀缺| 黄片小视频在线播放| 可以在线观看的亚洲视频| 中文字幕精品免费在线观看视频| 他把我摸到了高潮在线观看| 在线观看免费午夜福利视频| 亚洲一区二区三区不卡视频| 国产高清激情床上av| 国产熟女午夜一区二区三区| 又黄又粗又硬又大视频| 亚洲成人国产一区在线观看| 操出白浆在线播放| 一级a爱视频在线免费观看| 亚洲精品粉嫩美女一区| 老司机福利观看| 国产精品影院久久| 高清毛片免费观看视频网站| 欧美日本视频| 天堂√8在线中文| 欧美另类亚洲清纯唯美| 国内少妇人妻偷人精品xxx网站 | 日韩免费av在线播放| 在线观看午夜福利视频| 日本成人三级电影网站| 在线观看午夜福利视频| 久久香蕉精品热| 国产色视频综合| 老汉色∧v一级毛片| 在线观看免费日韩欧美大片| 麻豆成人av在线观看| 免费人成视频x8x8入口观看| 手机成人av网站| av超薄肉色丝袜交足视频| 成年版毛片免费区| 在线观看免费日韩欧美大片| 在线观看www视频免费| 又大又爽又粗| 黄色 视频免费看| 一进一出抽搐动态| 亚洲成人精品中文字幕电影| 成年女人毛片免费观看观看9| 村上凉子中文字幕在线| 久久久国产成人精品二区| 午夜福利18| 色综合欧美亚洲国产小说| 国产成人系列免费观看| 色av中文字幕| 国产私拍福利视频在线观看| 制服人妻中文乱码| 久久久水蜜桃国产精品网| 日韩大尺度精品在线看网址| 变态另类丝袜制服| 久久久久免费精品人妻一区二区 | 脱女人内裤的视频| 日韩视频一区二区在线观看| 美国免费a级毛片| 亚洲欧美一区二区三区黑人| 白带黄色成豆腐渣| 两性夫妻黄色片| 久久精品国产亚洲av高清一级| 久久精品亚洲精品国产色婷小说| 日本a在线网址| 亚洲午夜精品一区,二区,三区| 亚洲aⅴ乱码一区二区在线播放 | 久久精品国产清高在天天线| 伦理电影免费视频| АⅤ资源中文在线天堂| 99国产精品99久久久久| 一级a爱视频在线免费观看| 精品免费久久久久久久清纯| 成人国产综合亚洲| 大香蕉久久成人网| 老熟妇仑乱视频hdxx| 日韩有码中文字幕| 成人三级黄色视频| 亚洲成国产人片在线观看| 老熟妇仑乱视频hdxx| 久久婷婷人人爽人人干人人爱| 久久久国产精品麻豆| 狂野欧美激情性xxxx| 身体一侧抽搐| 午夜福利在线观看吧| 又黄又爽又免费观看的视频| 黄色视频,在线免费观看| 日日摸夜夜添夜夜添小说| 黄片小视频在线播放| 久久久精品国产亚洲av高清涩受| 每晚都被弄得嗷嗷叫到高潮| 欧美黑人巨大hd| 女性被躁到高潮视频| 成人亚洲精品一区在线观看| 久久热在线av| 他把我摸到了高潮在线观看| 成年女人毛片免费观看观看9| 久久久久久人人人人人| 一边摸一边抽搐一进一小说| 老司机深夜福利视频在线观看| 美女高潮到喷水免费观看| 国产aⅴ精品一区二区三区波| 啦啦啦免费观看视频1| 精品无人区乱码1区二区| 色哟哟哟哟哟哟| 黄片大片在线免费观看| 男男h啪啪无遮挡| 日韩精品免费视频一区二区三区| 女警被强在线播放| 老熟妇仑乱视频hdxx| 久久久久九九精品影院| 少妇熟女aⅴ在线视频| 国产一区二区三区在线臀色熟女| 人妻丰满熟妇av一区二区三区| 午夜a级毛片| 熟女少妇亚洲综合色aaa.| 免费看a级黄色片| 很黄的视频免费| 日韩欧美国产一区二区入口| 欧美乱妇无乱码| 精品一区二区三区视频在线观看免费| 视频在线观看一区二区三区| 色婷婷久久久亚洲欧美| 天天添夜夜摸| 韩国av一区二区三区四区| 国产一区二区在线av高清观看| 午夜久久久久精精品| av欧美777| 亚洲男人的天堂狠狠| 人人妻人人澡欧美一区二区| 亚洲一卡2卡3卡4卡5卡精品中文| 1024手机看黄色片| 老司机深夜福利视频在线观看| 亚洲男人天堂网一区| 欧美一级毛片孕妇| 丁香欧美五月| x7x7x7水蜜桃| 神马国产精品三级电影在线观看 | 亚洲激情在线av| 欧美国产日韩亚洲一区| 国产亚洲精品第一综合不卡| 久久香蕉激情| 少妇粗大呻吟视频| 麻豆国产av国片精品| 又黄又爽又免费观看的视频| 国产精品精品国产色婷婷| 日日摸夜夜添夜夜添小说| 99热只有精品国产| 国产精品一区二区免费欧美| 无遮挡黄片免费观看| 中文字幕精品亚洲无线码一区 | videosex国产| 久久久久久久久中文| 亚洲熟妇熟女久久| 成人欧美大片| 国产三级黄色录像| 看片在线看免费视频| 亚洲国产欧洲综合997久久, | 国产精品,欧美在线| 色尼玛亚洲综合影院| 亚洲精品美女久久久久99蜜臀| 亚洲午夜理论影院| 久久精品成人免费网站| 午夜视频精品福利| 亚洲国产中文字幕在线视频| 亚洲熟妇中文字幕五十中出| 9191精品国产免费久久| 黄色视频,在线免费观看| 人人妻人人看人人澡| 久久久久久国产a免费观看| 亚洲人成网站高清观看| 久久亚洲精品不卡| 午夜免费激情av| 伦理电影免费视频| 两个人免费观看高清视频| 久久精品国产99精品国产亚洲性色| 999久久久国产精品视频| 一进一出抽搐动态| 亚洲精品在线观看二区| 99在线视频只有这里精品首页| 久热爱精品视频在线9| 国产黄色小视频在线观看| 韩国av一区二区三区四区| 午夜福利免费观看在线| 亚洲av中文字字幕乱码综合 | 人妻久久中文字幕网| 亚洲国产精品999在线| 日本撒尿小便嘘嘘汇集6| ponron亚洲| 三级毛片av免费| 国产亚洲精品第一综合不卡| 精品无人区乱码1区二区| 韩国精品一区二区三区| 久99久视频精品免费| 午夜视频精品福利| 免费在线观看亚洲国产| 男人舔奶头视频| 国产单亲对白刺激| 啦啦啦免费观看视频1| 国产v大片淫在线免费观看| 国产成人系列免费观看| 久久久久久亚洲精品国产蜜桃av| 国产高清激情床上av| 成人欧美大片| 最近最新免费中文字幕在线| 国内少妇人妻偷人精品xxx网站 | 国产亚洲欧美98| 国产精品香港三级国产av潘金莲| 国产久久久一区二区三区| 国内揄拍国产精品人妻在线 | 香蕉丝袜av| 女人爽到高潮嗷嗷叫在线视频| 久久中文字幕人妻熟女| 中文字幕精品免费在线观看视频| 日本免费a在线| 一级黄色大片毛片| 香蕉国产在线看| 成人国产一区最新在线观看| 精品一区二区三区四区五区乱码| av天堂在线播放| 久久草成人影院| 欧美午夜高清在线| 国产99久久九九免费精品| 亚洲国产欧洲综合997久久, | av欧美777| 观看免费一级毛片| 色综合站精品国产| 又黄又粗又硬又大视频| 51午夜福利影视在线观看| 亚洲va日本ⅴa欧美va伊人久久| 日韩欧美国产一区二区入口| 国产日本99.免费观看| 男女午夜视频在线观看| 可以免费在线观看a视频的电影网站| 美女免费视频网站| 国产人伦9x9x在线观看| 欧美黄色片欧美黄色片| 中文字幕久久专区| 亚洲九九香蕉| 黄色成人免费大全| 亚洲人成电影免费在线| 国产男靠女视频免费网站| 成人国语在线视频| 精品国产美女av久久久久小说| xxx96com| 亚洲熟妇熟女久久| 亚洲成av人片免费观看| 色播在线永久视频| 亚洲 国产 在线| 成人手机av| 国产亚洲精品第一综合不卡| 好男人在线观看高清免费视频 | 母亲3免费完整高清在线观看| 精品福利观看| 成人手机av| av天堂在线播放|