Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

Impact of Fully Coupled Hydrology-Atmosphere Processes on Atmosphere Conditions: Investigating the Performance of the WRF-Hydro Model in the Three River Source Region on the Tibetan Plateau, China

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region.

S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), the boreal wintertime (November-April) Madden-Julian Oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (15 days). The slow-propagating MJO detours southward when traversing the maritime continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases.The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

Atlantic Niño/Niña Prediction Skills in NMME Models

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

Forecasting monthly numbers of hot days in Europe with a convolutional neural network

<p>Hot days, defined here as days exceeding the local 90th temperature percentile in summer months, pose an increasing threat to societies as summers warm along the climate. Therefore, an early warning of hot days and heat waves would be beneficial. To alleviate this need, we fit a convolutional neural network model to the global spatial distributions of the ERA5 reanalysis data to forecast the future number of hot days over the nearest 30-day period in Europe. <br><br>A large set of potential input variable candidates were explored, including variables from the stratosphere and from the surface layers. Three-fold cross-validation was used to find the optimal subset to be used in forecasting. In addition to the input variables themselves, we use their temporal differences as predictors. Stepwise backward increasing of the amount of fitting data was applied to study the sensitivity of modelling to the number of fitting years. Finally, to emulate the real forecasting, time series hindcasting was applied by fitting a new model for each forecasted year, using only years prior to each year for fitting. <br><br>The target variable – the number of hot days during the nearest month – is extremely season-dependent. The non-linear forecasting model can take this into account, and both the grid cell based numbers of hot days and especially the mean numbers inside sub-regions show that the model is capable of reproducing the numbers. The skill, measured by the anomaly correlation coefficient, increases rapidly and constantly with an increasing number of fitting years. Interestingly, the skill curve does not level out, implying the model could still be enhanced by further increasing the fitting data.</p>

Verification of the European subseasonal forecasts of wind speed and temperature

<p>The skill of predicted wind speed at 100 m and temperature at 2 m has been assessed in extended-range forecasts and hindcasts of the European Center for Medium-Range Weather Forecasts, starting from December 2015 to November 2019. The assessment was carried out over Europe grid-point wise and also by considering several spatially averaged country-sized domains, using standard scores such as the Continuous Ranked Probability Score and Anomaly Correlation Coefficient. The (re-)forecasts showed skill over climatology in predicting weekly mean wind speed and temperature well beyond two weeks. Even at a lead time of 6 weeks, the probability of the (re-)forecasts being skillful is greater than 50%, encouraging the use of operational subseasonal forecasts in the decision making value chain. The analysis also exhibited​ significant differences in skill in the predictability of different variables, with temperature being more skillful than wind speed, and for different seasons, with winter allowing more skillful forecasts. The predictability also displayed a clear spatial pattern with forecasts for temperature having more skill for Eastern than for Western Europe, and wind speed forecasts having more skill in Northern than Southern Europe.</p>

NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>

Predictable weather regimes at the S2S time scale

<p>State-of-the-art Subseasonal-to-Seasonal (S2S) forecast systems correctly simulate the main properties of weather regimes, like their spatial structures and their average frequencies. However, they are still unable to skillfully predict the observed frequencies of occurrence of weather regimes after the first ten days or so. Such a limitation severely restrict their application to develop climate service products, for example to forecast events with a strong impact on society, such as droughts, heat waves or cold spells.<br><br>This work describes two novel corrections that can be easily applied to any weather regime classification, to significantly enhance the S2S predictability of the frequencies of the weather regimes. The first one is based on the idea of weighting the daily observed anomaly fields of the variable used to cluster the atmospheric flow by the Anomaly Correlation Coefficient (ACC) of the same variable, just before clustering it. In this way, the clustering algorithm gives more importance to the areas where the forecast system is better in predicting the circulation variable. Thus, it is forced to generate the most predictable regimes. The second correction consists in the ACC weighting of the daily forecasted anomalies before the assignation of the daily fields to the observed regimes, to give more importance to the grid points where the forecast system has more skill. Hence, the forecasted time series of the regimes is more similar to the observed one.</p><p>Two sets of four regimes each were validated, one defined by <em>k-means</em> clustering of SLP from NCEP reanalysis over the Euro-Atlantic region during lasts 40-years (1979-2018) for October to March, and another for April to September. Forecasts proceed from the 2018 version of the Monthly Forecast System developed by the European Centre for Medium-Range Weather Forecasts (ECMWF-MFS). Predictability was measured in cross-validation by the Pearson correlations between the forecasted and observed weekly frequencies of occurrence of the regimes, for each of the 52 weekly start dates of the year separately and for a 20-years hindcast period (1998-2017).<br><br>Results show that with both corrections described above, Pearson correlations increase up to r = +0.5, depending on the start date and forecast time. Average increase over all start dates is of r = +0.2 at forecast days 12-18 and r = +0.3 at forecast days 19-25 and 26-32. The gain is spread quite evenly along the start dates of the year. <br><br>Beyond the Euro-Atlantic region, these two corrections can be easily transferred to any area of the world. They may be employed to correct seasonal predictions of weather regimes too (results in progress). Besides, their application is straightforward and provides a significant skill gain at a negligible computational cost for potentially all S2S forecast systems and regime classifications. We foresee that they might also benefit forecasts of atmospheric teleconnections. For all these reasons, we warmly recommend the S2S community to take advantage of this 'low-hanging fruit'.<br> </p>

Seasonal forecasting of the Atlantic Niño

<p>The Atlantic Niño is the leading mode of interannual variability in the Tropical Atlantic, which has impacts not only on the African monsoon but also in remote regions. In the present study, we investigate the predictability of the Atlantic Niño's mature phase (June-July) at seasonal time-scale, as well as its conditioning. We analyze a large set of state-of-the art forecasts systems from the North American Multi-Model Ensemble (NMME) and Copernicus Climate Change Service (C3S) multi-models. The prediction skill of the ATL3 index has considerably improved as compared to previous forecast quality assessments, with Anomaly Correlation Coefficient (ACC) reaching up to 0.8 for the May start date. Most of the models achieve skillful prediction of the Atlantic Niño from the May start-date, and some outperform persistence. For the start-dates of April, March and February, most of the models perform better than persistence and some achieve significant correlation skill for ATL3. While there has been improvement in forecasting capability, overall the warm SST bias and associated drift remain large in the equatorial Atlantic in most of the systems. Our results suggests that the skill in predicting the Atlantic Niño in summer is weakly related to the local SST drift during the first month of the forecast, but not to the magnitude of the SST bias during the peak. In addition, we find evidence that the skill in the equatorial Atlantic is related to the ability of the models to properly represent the large-scale atmospheric circulation pattern in the South Atlantic (i.e. St. Helena high).</p>

Seasonal Forecasting of the Onset of the Rainy Season in West Africa

Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an improved visualization of probabilistic ORS forecasts. The performance of the approach is tested for several climate zones (the Sahel, Sudan and Guinean zone) in West Africa for a period of eleven years (2000 to 2010), using hindcasts from the Seasonal Forecasting System 4 of ECMWF. We indicated that seasonal ORS forecasts can be skillful for individual years and specific regions (e.g., the Guinean coasts), but also associated with large uncertainties. A spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures (e.g., the anomaly correlation coefficient) to avoid an overestimation of the forecast skill. The graphical methods consist of several common formats used in seasonal forecasting and a new index-based method for a quicker interpretation of probabilistic ORS forecast. The new index can also be applied to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. Moreover, the forecasting approach proposed in this study is not computationally intensive and is therefore operational applicable for forecasting centers in tropical and subtropical regions where computing power and bandwidth are often limited.