Quarterly Journal of the Royal Meteorological Society

doi: 10.1002/qj.4695pmid: N/A

Wang, Yuanbing; Qian, Xinyao; Chen, Yaodeng; Min, Jinzhong

doi: 10.1002/qj.4671pmid: N/A

Large‐scale environment fields play an important role in the accurate prediction of typhoons. However, regional predictions for typhoons often suffer from inadequate representation of large‐scale flow pattern such as those from global models due to limited domain size and observations employed in regional models, especially when multiple typhoons that interact concurrently occur in a regional domain. This study merges the large‐scale information from global model forecasts with the mesoscale information from regional model forecasts in the hybrid ensemble‐variational (EnVar) data assimilation by adding an analysis constraint in the EnVar cost function, which is defined by the departure of the regional model EnVar analysis from the global model fields and takes advantage of flow‐dependent ensemble background error covariance for the introduction of large scales using data assimilation. The EnVar assimilation impacts of the large‐scale fields on predictions of triple typhoons are assessed by conducting cycling assimilation and forecast experiments for a 13‐day‐long period in July 2015 when three typhoons concurrently occurred. Results show that the large‐scale constraint for EnVar can clearly improve the triple‐typhoons' track and intensity forecasts of the regional model. The large‐scale information introduced by the proposed method is also shown to reduce forecast errors of wind, temperature and humidity, respectively. Predictions of the rainfall caused by typhoons are also ameliorated. Besides, the analysis‐constrained regional predictions provide better model dynamic fields in terms of sea surface pressure, geopotential height, and water vapor transport, as well as developed typhoon structures. In addition, the adaptive bias correction for radiance assimilation presents a stable performance under the influence of introducing extra background large‐scale fields. The results indicate that the large‐scale analysis constraint introduced in the hybrid EnVar takes advantages of the multiscale information from the global model and the regional model respectively, thus improving the final results of the predictions of multiple typhoons.

Tempest, Kirsten I.; Craig, George C.; Puh, Matjaž; Keil, Christian

doi: 10.1002/qj.4684pmid: N/A

The constraint of computational power and the huge number of degrees of freedom of the atmosphere means a sampling uncertainty exists in probabilistic ensemble forecasts. In our previous study, the uncertainty could be quantified, creating a convergence measure which converges proportional to n−1/2 in the limit of large ensemble size n. This power law can then be extrapolated to determine how sampling uncertainty would decrease with larger ensemble sizes and hence find the necessary ensemble size. It is unknown, however, how the sampling uncertainty depends on different weather regimes. This study extends the previous idealised ensemble developed, by including weak and strong forcing convective weather regimes, to look at how sampling uncertainty convergence differs in each. Two 5,000‐member ensembles were run, with weak and strong forcing respectively. Comparisons with a kilometre‐scale weather prediction model ensured realistic weak and strong forcing regimes by comparing the rain, convective available potential energy (CAPE), convective adjustment timescale, and distribution shapes throughout the diurnal cycle. Differences in distribution shape between the regimes led to differences in the convergence measure. Large differences in spread between weak and strong forcing runs throughout the 24 hr period led to large differences in sampling uncertainty of the mean and standard deviation, which could be quantified according to well‐known equations. The timing of these differences was case‐dependent. For extreme statistics such as the 0.95 quantile and for cases where there was precipitation, the moisture variables for the weak forcing case had the largest sampling uncertainty and required the most members for convergence proportional to n−1/2. This was due to the tails of the weak forcing moisture variables containing the least amount of density. Different ensemble sizes will hence be required depending on whether one is in the weak or strong forcing convective weather regime.

Salonen, Kirsti; August, Thomas; Hultberg, Tim; Benedetti, Angela; McNally, Anthony

doi: 10.1002/qj.4709pmid: N/A

The potential of assimilating Infrared Atmospheric Sounding Interferometer (IASI) temperature and humidity retrievals with their scene‐dependent observation operators, a product developed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), has been investigated in the European Centre for Medium‐Range Weather Forecasts (ECMWF) system. The results are compared with the corresponding radiance assimilation impact. The experiments are done in a depleted observing‐system framework to emphasise the impact originating from the new data. The focus in the first assimilation experiments has been on retrievals over sea and in clear‐sky scenes, as they have high and homogeneous quality. The results demonstrate a clear statistically significant positive impact on temperature, humidity, and wind forecasts. However, the impact is somewhat smaller in magnitude for the retrieval assimilation compared with the radiance assimilation above 700 hPa.

Udina, Mireia; Peinó, Eric; Polls, Francesc; Mercader, Jordi; Guerrero, Iciar; Valmassoi, Arianna; Paci, Alexandre; Bech, Joan

doi: 10.1002/qj.4756pmid: N/A

The Land Surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment (LIAISE) campaign examined the impact of anthropization on the water cycle in terms of land–atmosphere–hydrology interactions. The objective of this study is to assess the effects of irrigation on the atmosphere and on precipitation in Weather Research and Forecasting model simulations during the LIAISE special observation period in July 2021. Comparisons between simulations and observations show better verification scores for air temperature, humidity, and wind speed and direction when the model included the irrigation parametrization, improving the model warm and dry bias at 2 m over irrigated areas. Other changes found are the weakening of the sea breeze circulation and a more realistic surface energy partitioning representation. The boundary‐layer height is lowered in the vicinity of irrigated areas, causing a decrease in the lifting condensation level and the level of free convection, which induce increases in convective available potential energy and convective inhibition. Precipitation differences between simulations become relevant for smaller areas, close to the irrigated land. When convection is parametrized, simulations including irrigation tend to produce a decrease in rainfall (negative feedback), whereas convection‐permitting simulations produce an increase (positive feedback), although the latter underestimates substantially the observed precipitation field. In addition, irrigation activation decreases the areas exceeding moderate hourly precipitation intensities in all simulations. There is a local impact of irrigated land on model‐resolved precipitation accumulations and intensities, although including the irrigation parametrization did not improve the representation of the observed precipitation field, as probably the precipitation systems during the LIAISE special observation period in July 2021 were mostly driven by larger scale perturbations or mesoscale systems, more than by local processes. Results reported here not only contribute to enhance our understanding of irrigation effects upon precipitation but also demonstrate the need to include irrigation parametrizations in numerical forecasts to overcome the biases found.

Kulkarni, Akshay; Raju, P. V. S.; Ashrit, Raghavendra; Sagalgile, Archana; Singh, Bhupendra Bahadur; Prasad, Jagdish

doi: 10.1002/qj.4757pmid: N/A

The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

Fu, He; Guo, Jianing; Deng, Chenguang; Liu, Heng; Wu, Jie; Shi, Zhengguo; Wang, Cailing; Xie, Xiaoning

doi: 10.1002/qj.4759pmid: N/A

The middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi‐arid and semi‐humid climates. As one of the climate‐sensitive regions in China, MRYR has a fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses, and mudslides caused by extreme precipitation. However, scarceness of high‐resolution precipitation data over MRYR limits assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this article, we design a Residual‐in‐Residual Dense Block based Network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with a generalized linear regression model (GLM) and two popular deep‐learning‐based models. The multi‐level residuals and dense connectivity strategies introduced in RRDBNet help it to learn more abstract features and complex nonlinear relationships among climate variables to improve downscaling performance. The results show that the proposed RRDBNet has good performance in precipitation simulations, which can reproduce the spatial–temporal characteristics of high‐resolution precipitation well. RRDBNet reduces the root‐mean‐squared error (RMSE) by 19% and improves the Pearson correlation coefficient (CC) by 6% relative to GLM for climatology mean precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. It reduces RMSE by 58% (79%) and improves CC by 38% (145%) relative to GLM for R95P (R99P), where R95P and R99P represent extreme precipitation and very extreme precipitation, respectively. For the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.

Li, Yonghui; Han, Wei; Duan, Wansuo

doi: 10.1002/qj.4760pmid: N/A

Target observations have garnered significant attention owing to their successful applications in enhancing forecasting skills of extreme weather events, particularly tropical cyclone (TC) events. The key step of implementing target observation is to determine the sensitive area in advance. Previous studies often obtained the sensitive areas for TC forecasting by vertically integrating the energy of optimal perturbation and taking the horizontal area of large energy, in an attempt to use it to represent roughly the sensitivity of the whole atmospheric layer. The advent of the geostationary interferometric infrared sounder on the FY‐4A satellite and then corresponding satellite data assimilation have opened up a new possibility for identifying the vertical sensitivity for TC forecasting to improve the forecasting skill. This article proposes a targeting satellite channel (TSC) approach to accurately capture the sensitivity along vertical directions of the atmosphere that allows one to preferentially select the channels whose observations locate on the sensitive vertical atmospheric layers. Numerical experiments demonstrate that, when preferentially assimilating the channel observations obtained from the TSC approach, the TC tracks achieve a considerably smaller forecast error than the information entropy channel selection approach. The TSC approach, therefore, has the potential for the satellite data assimilation to improve TC track forecasting skill very effectively, which can also provide guidance to targeting observations in field campaigns for TC forecasting.

Van Weverberg, K.; Ghilain, N.; Goudenhoofdt, E.; Barbier, M.; Koistinen, E.; Doutreloup, S.; Van Schaeybroeck, B.; Frankl, A.; Field, P.

doi: 10.1002/qj.4761pmid: N/A

This article presents an evaluation and sensitivity analysis of km‐scale simulations of an unprecedented extreme rainfall event over Europe, with a specific focus on sub‐hourly extremes, size distributions, and kinetic energy (KE) of rain. These variables are critical for hydrological applications, such as flood forecasting or soil‐loss monitoring, but are rarely directly obtained from numerical weather prediction (NWP) models. The simulations presented here reproduce the overall characteristics of the event, but overestimate the extreme rain rates. The rain rate–KE relation was well‐captured, despite too large volume‐mean drop diameters. Amongst the sensitivities investigated, the representation of the raindrop self‐collection–breakup equilibrium and the raindrop size‐distribution shape were found to have the most profound impact on the rainfall characteristics. While extreme rain rates varied within 30%, the rain KE varied by a factor of four between the realistic perturbations to the microphysical assumptions. Changes to the aerosol concentration and rain terminal velocity relations were found to have a relatively smaller impact. Given the large uncertainties, a continued effort to improve the model physics will be indispensable to estimate rain intensities and KE reliably for direct hydrological applications.

Mosso, Samuele; Calaf, Marc; Stiperski, Ivana

doi: 10.1002/qj.4762pmid: N/A

Monin–Obukhov similarity theory (MOST) is used in virtually all Earth System Models to parametrize the near‐surface turbulent exchanges and mean variable profiles. Despite its widespread use, there is high uncertainty in the literature about the appropriate parametrizations to use. In addition, MOST has limitations in very stable and unstable regimes, over heterogeneous terrain and complex orography, and has been found to represent the surface fluxes incorrectly. A new approach including turbulence anisotropy as a non‐dimensional scaling parameter has recently been developed and has been shown to overcome these limitations and generalize the flux‐variance relations to complex terrain. In this article, we analyze the flux‐gradient relations for five well‐known datasets, ranging from flat and homogeneous to slightly complex terrain. The scaling relations show substantial scatter and highlight the uncertainty in the choice of parametrization even over canonical conditions. We show that, by including information on turbulence anisotropy as an additional scaling parameter, the original scatter becomes well bounded and new formulations can be developed that drastically improve the accuracy of the flux‐gradient relations for wind shear (ϕM) in unstable conditions and for temperature gradient (ϕH) in both unstable and stable regimes. This analysis shows that both ϕM and ϕH are strongly dependent on turbulence anisotropy and allows us finally to settle the extensively discussed free convection regime for ϕM, which clearly exhibits a +1/3 power law when anisotropy is accounted for. Furthermore, we show that the eddy diffusivities for momentum and heat and the turbulent Prandtl number are strongly dependent on anisotropy and that the latter goes to zero in the free convection limit. These results highlight the necessity to include anisotropy in the study of near‐surface atmospheric turbulence and lead the way for theoretically more robust simulations of the boundary layer over complex terrain.