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Extratropical cyclones are often associated with heavy precipitation events and can have major socio‐economic impacts. This study investigates how much of the total precipitation in the Northern Hemisphere is associated with extratropical cyclones. An objective feature tracking algorithm is used to locate cyclones and the precipitation associated with these cyclones is quantified to establish their contribution to total precipitation. Climatologies are produced from the Global Precipitation Climatology Project (GPCP) daily dataset and the ERA‐Interim reanalysis. The magnitude and spatial distribution of cyclone associated precipitation and their percentage contribution to total precipitation is closely comparable in both datasets. In some regions, the contribution of extratropical cyclones exceeds 90/85% of the total DJF/JJA precipitation climatology. The relative contribution of the most intensely precipitating storms to total precipitation is greater in DJF than JJA. The most intensely precipitating 10% of storms contribute over 20% of total storm associated precipitation in DJF, whereas they provide less than 15% of this total in JJA. Key Points Extratropical cyclones contribute over 50% of NH precipitation Cyclones contribute up to 90/85% of precipitation in some regions in DJF/JJA The most intense storms contribute significantly to the total climatology

Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

The new generation of weather observatory satellites, namely Global Precipitation Measurement (GPM) constellation satellites, is the lead observatory of the 10 highly advanced earth orbiting weather research satellites. Indeed, GPM is the first satellite that has been designed to measure light rain and snowfall, in addition to heavy tropical rainfall. This work compares the final run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, the post real time of TRMM and Multi-satellite Precipitation Analysis (TMPA-3B42) and the Era-Interim product from the European Centre for Medium Range Weather Forecasts (ECMWF) against the Iran Meteorological Organization (IMO) daily precipitation measured by the synoptic rain-gauges over four regions with different topography and climate conditions in Iran. Assessment is implemented for a one-year period from March 2014 to February 2015. Overall, in daily scale the results reveal that all three products lead to underestimation but IMERG performs better than other products and underestimates precipitation slightly in all four regions. Based on monthly and seasonal scale, in Guilan all products, in Bushehr and Kermanshah ERA-Interim and in Tehran IMERG and ERA-Interim tend to underestimate. The correlation coefficient between IMERG and the rain-gauge data in daily scale is far superior to that of Era-Interim and TMPA-3B42. On the basis of daily timescale of bias in comparison with the ground data, the IMERG product far outperforms ERA-Interim and 3B42 products. According to the categorical verification technique in this study, IMERG yields better results for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR) in those areas with stratiform and orographic precipitation, such as Tehran and Kermanshah, compared with other satellite/model data sets. In particular, for heavy precipitation (>15 mm/day), IMERG is superior to the other products in all study areas and could be used in future for meteorological and hydrological models, etc.

The efficacy of global and regional datasets on the prediction of extremely severe cyclonic storms over the Bay of Bengal (BoB) was evaluated using the Weather Research and Forecasting (WRF) model in a double‐nested domain with a 4 km finer resolution on three different datasets, namely FNL, ERA‐Interim, and Indian Monsoon Data Assimilation and Analysis (IMDAA). The initial cyclonic vortex, the vertical profile of horizontal wind speed, and relative humidity from different datasets were assessed to evaluate the initial structure and validated with IMD best‐fit track data. The model results highlight that simulations with FNL data predict tracks and intensities more accurately for the majority of cyclonic storms compared with the IMDAA and ERA‐Interim datasets. Simulations with FNL data exhibit the least mean track errors of 70, 126, 121, and 204 km for days 1–4, respectively. Additionally, the mean wind error of five extremely severe cyclonic storms (ESCSs) using FNL data is approximately 9.3, 4.6, 7.7, and 10.9 m/s, respectively, from day 1 to day 4. It is observed that the regional reanalysis of IMDAA datasets outperformed the forecast of several parameters such as maximum surface wind speed, central sea level pressure, and rainfall for the ESCSs Fani and Sidr. The FNL dataset overpredicted the amount of 24‐h accumulated rainfall compared with the ERA‐Interim and IMDAA datasets, whereas the IMDAA dataset performed better with lower values of root mean square error (148 mm/day), standard deviation (124 mm/day), and higher correlation (0.68) with the TRMM dataset. Model predictions highlight that the regional dataset IMDAA performs better in predicting rainfall magnitude compared with the global dataset due to the added assimilation of numerous local observations. The regional dataset could be improved by exploring large‐scale circulation features and their significant role in predicting the track, intensity, and landfall location of the tropical cyclones. (a) Tracks of five extremely severe tropical cyclones—Amphan, Fani, Hudhud, Phailin, and Sidr over the Bay of Bengal (BoB); and (b) geopotential height with overlaid model domains over the BOB region for the simulation of tropical cyclones. The model results highlight that simulations with FNL data predict tracks and intensities more accurately for the majority of cyclonic storms compared with the IMDAA and ERA‐Interim datasets. Model predictions highlight that the regional dataset IMDAA performs better in predicting rainfall magnitude compared with the global dataset due to the added assimilation of numerous local observations.

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980-2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.

This study examines the progress made by reanalyses and satellite products in the estimation of cloud cover over China: the ECMWF reanalyses ERA5 and ERA-Interim, geostationary satellite observation Himawari-8 (H8) and the International Satellite Cloud Climatology Project H-series (ISCCP) product. There is great similarity in spatial patterns of cloud cover in reanalyses and satellite observations, especially between ERA5 and H8. Distinct characteristics of the seasonal evolution of cloud cover are shown over the Tibetan Plateau (TP), the southeast (SE) and northeast (NE) of China. Differences in magnitudes of cloud cover exist. Overestimations are about 10% for reanalyses and about 20% for ISCCP in compared with certain cloud cover in H8. When probable cloud (about 10%) in H8 is included in the estimation, biases reduce the most in ERA5. The cloud hit rate (CHR) and false alarm rate (FAR) in against H8 and ISCCP reveal that simulated clouds in ERA5 have been improved especially over eastern China, but with limited improvement over TP in compared with ERA-Interim. Diurnal variations of cloud cover are characterized by increases during daytime over those three regions. Amplifications of diurnal variation vary over different regions and months. Satellite observations and ERA5 indicate distinguished diurnal cycle of cloud cover over TP, while further investigation based on ERA5 reveals coherent diurnal cycle in meteorological environment. Long-term changes of cloud cover highlight decreasing trends over TP and particular during March in past decades based on ISCCP and ERA5, which require further investigation in future.

The latest ECMWF global reanalysis, ERA5, is able to provide hourly surface pressure and water vapor-weighted mean temperature ( T m ), which are two key factors in GPS precipitable water vapor (PWV) retrieval. Performance of surface pressure, surface air temperature, and T m derived from ERA5 and its predecessor ERA-Interim (ERAI) are evaluated by comparing with more than 2000 meteorological stations and 89 radiosonde stations in the year of 2016 over China. Average pressure error RMS is 0.7 hPa for ERA5, compared to 1.0 hPa for ERAI, and ERA5 pressure diurnal variations agree much better than ERAI with in situ measurements. Temperature and T m differences between ERA5 and ERAI are relatively smaller, with error RMS of 1.8 K and 1.6 K for ERA5-derived temperature and T m , respectively. PWV error contributed by reanalysis-derived parameters is also estimated. The ERA5-induced PWV error is generally less than 1 mm, with smaller errors (< 0.4 mm) in eastern China but larger errors (can exceed 0.6 mm) in northwestern China and in the southeast of the Tibetan Plateau. Diurnal variations of PWV retrieved using pressure and T m from meteorological measurements (MET) and reanalysis products are compared. Good agreements are found between ERA5-based PWV and MET-based PWV in diurnal variations, while artificial diurnal signals are introduced in ERAI-based PWV, especially in the Tibetan Plateau. This study indicates that ERA5 can support high-accuracy hourly GPS PWV retrieval over China without contaminating the diurnal cycles, which is of great importance for historical GPS PWV retrieval at stations without collocated meteorological sensors equipped.

To assess the impact of the vertical velocity scheme on modeling transport in the tropical tropopause layer (TTL), 3 month backward trajectories are initialized in the TTL for boreal winter and summer 2002. The calculations are done in either a kinematic scenario with pressure tendency as the vertical velocity or in a diabatic scenario with cross‐isentropic velocity deduced from various diabatic heating rates due to radiation (clear sky, all sky) and latent, diffusive and turbulent heating. This work provides a guideline for assessing the sensitivity of trajectory and chemical transport model (CTM) results on the choice of the vertical velocity scheme. We find that many transport characteristics, such as time scales, pathways and dispersion, crucially depend on the vertical velocity scheme. The strongest tropical upwelling results from the operational European Centre for Medium‐Range Weather Forecasts kinematic scenario with the time scale for ascending from 340 to 400 K of 1 month. For the ERA‐Interim kinematic and total diabatic scenarios, this time scale is about 2 months, and for the all‐sky scenario it is as long as 2.5 months. In a diabatic scenario, the whole TTL exhibits mean upward motion, whereas in a kinematic scenario, regions of subsidence occur in the upper TTL. However, some transport characteristics robustly emerge from the different scenarios, such as an enhancement of residence times between 350 and 380 K and a strong impact of meridional in‐mixing from the extratropics on the composition of the TTL. Moreover, an increase of meridionally transported air from the summer hemisphere into the TTL (maximum for boreal summer) is found as an invariant feature among all the scenarios.

Monthly wind data from 506 meteorological stations and ERA-Interim reanalysis during 1991–2015, are used to examine the surface wind trend over East China. Furthermore, combining the urbanization information derived from the DMSP/OLS nighttime light data during 1992–2013, the effects of urbanization on surface wind change are investigated by applying the observation minus reanalysis (OMR) method. The results show that the observed surface wind speed over East China is distinctly weakening with a rate of −0.16 m s−1 deca−1 during 1991–2015, while ERA-Interim wind speed does not have significant decreasing or increasing trend in the same period. The observed surface wind declining is mainly attributed to underlying surface changes of stations observational areas that were mostly induced by the urbanization in East China. Moreover, the wind declining intensity is closely related to the urbanization rhythms. The OMR annual surface wind speeds of Rhythm-VS, Rhythm-S, Rhythm-M, Rhythm-F and Rhythm-VF, have decreasing trends with the rates of −0.02 to −0.09, −0.16 to −0.26, −0.22 to −0.30, −0.26 to −0.36 and −0.33 to −0.51 m s−1 deca−1, respectively. The faster urbanization rhythm is, the stronger wind speed weakening presents. Additionally urban expansion is another factor resulted in the observed surface wind declining.