Quarterly Journal of the Royal Meteorological Society

doi: 10.1002/qj.4067pmid: N/A

Ahmed, Tanvir; Lee, Joohyun; Jin, Han‐Gyul; Baik, Jong‐Jin

doi: 10.1002/qj.4246pmid: N/A

The Meghalaya Plateau (MP) located in northeast India is one of the rainiest regions in the world. On 18–19 August 2015, Mawsynram on the southern slope of MP received 745 mm of precipitation in 24 hr. This study investigates the dynamical, thermodynamical and cloud microphysical processes associated with this event through numerical simulations with fine horizontal resolutions (1 and 1/3 km). The control (CNTL) simulation with 1 km grid spacing successfully reproduces the observed spatial pattern of accumulated precipitation. A simulation without MP (noMP) is carried out to examine the role of MP in this precipitation. From 1500 LST 18 to 0000 LST 19 (P1) when the low‐level jet carrying warm and moist air towards MP is relatively weak, the upslope region receives a moderate amount of precipitation which is initiated over this region due to the orographic lifting, while almost no precipitation is received there in the noMP simulation. Warm microphysical processes play dominant roles in the precipitation in P1. From 0000 to 0900 LST 19 (P2) when the low‐level jet is enhanced, the CNTL simulation shows very heavy precipitation in the upslope region, much heavier than that in the noMP simulation. Deep convective systems developed upwind of MP move towards MP. These convective systems merge together and strengthen over the upslope region. The accretion process is substantially enhanced by the vigorous updraughts at low levels over the steep slope of MP, resulting in heavy precipitation. The 1/3 km resolution simulation shows much heavier precipitation in the upslope region than the CNTL simulation. The increased horizontal resolution makes the slopes steeper, resulting in further intensification of the updraughts over this region. This increase in simulated precipitation reduces the deviation from the rain‐gauge observation, implying the importance of very high horizontal resolutions in simulating extremely heavy precipitation in MP.

Dufée, Benjamin; Mémin, Etienne; Crisan, Dan

doi: 10.1002/qj.4247pmid: N/A

We investigate the application of a stochastic dynamical model in ensemble Kalman filter methods. Ensemble Kalman filters are very popular in data assimilation because of their ability to handle the filtering of high‐dimensional systems with reasonably small ensembles (especially when they are accompanied with so‐called localization techniques). The stochastic framework presented here relies on location uncertainty principles that model the effects of the model errors on the large‐scale flow components. The experiments carried out on the surface quasi‐geostrophic model with the localized square‐root filter demonstrate two significant improvements compared with the deterministic framework. First, as the uncertainty is a priori built into the model through the stochastic parametrization, there is no need for ad hoc variance inflation or perturbation of the initial condition. Second, it yields better mean‐square‐error results than the deterministic ones.

Carminati, Fabien

doi: 10.1002/qj.4248pmid: N/A

A new channel selection is proposed for the processing of observations at full spectral resolution (FSR) from the Cross‐track Infrared Sounder (CrIS) and Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments in the Met Office global numerical weather prediction (NWP) system. The new selection has been derived in order to minimise the error in NWP analysis and has been compared to an existing selection developed at the National Oceanic and Atmospheric Administration (NOAA). Both selections have been tested in the Met Office global NWP system to investigate the use of FSR CrIS compared with the current normal spectral resolution (NSR) set‐up employed in operations. Improvements are obtained for most forecast variables up to 7‐day lead times, with change in root‐mean‐square error (RMSE) ranging from 0.13 to 0.54%, although degradation of tropical temperatures are noted for the NOAA selection. The background fit to observations from independent sounders improves by up to 0.8% with the new selection, outperforming the NOAA selection which results in degradations across most instruments. The assimilation of HIRAS observations does not however benefit the system.

Marcheggiani, Andrea; Ambaum, Maarten H. P.; Messori, Gabriele

doi: 10.1002/qj.4249pmid: 35874118

Covariance between meridional wind and air temperature in the lower troposphere quantifies the poleward flux of dry static energy in the atmosphere; in the midlatitudes, this is primarily realised by baroclinic weather systems. It is shown that strong covariance between temperature and meridional wind results from both enhanced correlation and enhanced variance, and that the two evolve according to a distinct temporal structure akin to a life‐cycle. Starting from a state of low correlation and variance, there is first a gradual build‐up to modal growth at constant, high correlation, followed by a rapid decay at relatively low correlation values. This life‐cycle evolution is observed most markedly over oceanic regions, and cannot be explained on purely statistical grounds. We find that local peaks of meridional heat flux are not exclusively linked to the action of individual weather systems and can affect the atmospheric circulation on larger length‐scales through wave propagation along waveguides.

Vallis, Geoffrey K.

doi: 10.1002/qj.4250pmid: N/A

In their comment, Davison and Haynes remark on an apparent time‐step sensitivity in the model presented by Vallis and Penn, such that in their own simulations the excitability is lost when using a smaller time step. However, this reply shows that if the condensational time‐scale is suitably small then excitable behaviour can be achieved over a range of time steps, including very small ones, with very similar energy levels and large‐scale behaviour at both large and small time steps. In particular, an eastward‐propagating disturbance resembling the Madden–Julian Oscillation can still be reproduced, independent of time step. Certainly there are numerical, mathematical, and physical difficulties in understanding a system in which condensation occurs on short time‐scales, and this reply discusses further the applicability of the results to the real atmosphere.

Penland, Cécile; Sardeshmukh, Prashant D.

doi: 10.1002/qj.4251pmid: N/A

It is often not appreciated that forecast ensembles are generally skewed. The skew can arise from the state dependence of the chaotic system dynamics responsible for the ensemble spread. Generation of skew by this mechanism can be demonstrated in even the simplest dynamical system with state‐dependent noise, and even when the initial and the asymptotic (i.e., the “climatological”) forecast distributions are both symmetric. Indeed, forecast distributions of systems with state‐dependent noise in the dynamical tendencies must in general be both skewed and heavy tailed, with implications for forecasting extreme anomaly risks. Ensemble forecast systems that misrepresent such state‐dependent noise have state‐dependent errors in their forecast probability distributions. Because such errors depend on both the initial condition and forecast lead time, they cannot be removed by simple a posteriori bias corrections of the forecast distributions. The ensemble standard deviation is often used as a simple metric of ensemble spread even when the forecast distribution is not Gaussian. In a similar spirit, the ensemble skew S may be used as a simple metric of the difference D between the ensemble‐mean and most likely forecast, as well as the risk ratio R of extreme positive and negative deviations from the ensemble‐mean forecast. This is motivated by the facts that (1) the probability distributions of many geophysical quantities are approximately stochastically generated skewed (SGS) distributions, for which simple analytical relationships exist between these quantities, and (2) Gaussian distributions are a sub‐class of SGS distributions. However, S may serve as a useful metric of R and D even when the distributions are not strictly SGS distributions.

Mignac, Davi; Martin, Matthew; Fiedler, Emma; Blockley, Ed; Fournier, Nicolas

doi: 10.1002/qj.4252pmid: N/A

Derived from two complementary satellites, CryoSat‐2 and Soil Moisture and Ocean Salinity (SMOS), sea ice thickness (SIT) data are assimilated into the Met Office's global ocean–sea ice forecasting system, FOAM, using a 3D‐Var assimilation scheme, NEMOVAR. CryoSat‐2 along‐track SITs, which are converted from freeboard measurements using the model snow depth, and a daily, gridded SMOS SIT product are used in the assimilation to constrain the Arctic sea ice thickness. When using only CryoSat‐2 assimilation, SIT forecast fields within the ice pack are greatly improved with respect to independent airborne measurements. However, the positive impacts of CryoSat‐2 assimilation in thick ice regions are counteracted by an SIT overestimation in areas of thin ice, due to biased freeboard measurements there. Adding the SMOS assimilation results in much thinner SITs in those regions, which performs better than the control when compared to SIT objective analyses and mooring measurements in the Beaufort and Barents Seas. Furthermore, SMOS assimilation enhances the short‐term predictive skill of the marginal sea‐ice concentration relative to the control. This is translated into a consistent retreat of the sea‐ice covered areas in the 5‐day forecasts during March 2017, which is in better agreement with independent ice edge products. This work successfully demonstrates improvements in FOAM sea ice when SIT observations from both CryoSat‐2 and SMOS are assimilated, representing an important step towards the operational implementation of SIT assimilation within Met Office forecasting systems.

Warren, Elliott; Charlton‐Perez, Cristina; Lean, Humphrey; Kotthaus, Simone; Grimmond, Sue

doi: 10.1002/qj.4253pmid: 35915744

Sensors that measure the attenuated backscatter coefficient (e.g., automatic lidars and ceilometers [ALCs]) provide information on aerosols that can impact urban climate and human health. To design an observational network of ALC sensors for supporting data assimilation and to improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio‐temporal patterns of aerosol‐attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model [LM]), for 28 clear‐sky days and nights. Initially, attenuated backscatter coefficient data are analysed using S‐mode principal component analysis (PCA) with varimax rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level, with common EOFs found across different heights (day and night) for both NWP models. EOFs relate strongly to orography, wind, and emissions source location, highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban–rural differences are largest when wind speeds are low and vertical boundary‐layer dynamics can more effectively distribute near‐surface aerosol emissions vertically. In several night‐time EOFs, gravity‐wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. PCA output, with agglomerative Ward cluster analysis (CA), minimises intra‐group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA‐CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here, CA is used with wind roses to suggest the optimised ALC deployment is one in the city to observe the urban plume and others surrounding the city, with priority given to cluster size and frequency of upwind advection.

Dang, Ruijun; Qiu, Xiaobin; Yang, Yi; Zhang, Shuwen

doi: 10.1002/qj.4254pmid: N/A

The planetary boundary‐layer height (PBLH) is a key parameter that is very important in numerical weather and air quality predictions. The current LiDAR networks make it possible to provide potential PBLH observations, and assimilating the parameter will be helpful to improve the forecast of variables within the planetary boundary layer (PBL). This study first carried out idealized experiments on PBLH assimilation through observation system simulation experiments (OSSEs). The ensemble square root filter (EnSRF) is applied to assimilate the simulated PBLH based on the Weather Research and Forecast (WRF) model. This study mainly focused on two issues: which variables can be effectively improved by assimilating the PBLH, and whether there are differences in the assimilation effects above and within the PBL in the vertical direction. The results show that during the daytime within the PBL, PBLH assimilation could effectively improve the simulation and forecasting of model variables, including perturbation potential temperature (pt), water vapour mixing ratio (qv) and perturbation geopotential (ph); meanwhile, PBLH assimilation could strengthen turbulence mixing and result in a warmer and drier PBL. While the vertical and horizontal wind speeds (w, u and v) cannot be effectively updated by assimilating the PBLH, they could be indirectly improved by model dynamical adjustment when pt, qv and ph are improved. Above the PBL top, most of the above six variables cannot be effectively improved by assimilating the daytime PBLH. In contrast, at night, only u and v within the PBL could be directly updated by the low nocturnal PBLH. In this study, the simplest and most idealized schemes are selected based on idealized experiments; nevertheless, the results lay a foundation for later vertical and horizontal localization research and for the study of LiDAR‐retrieved PBLH assimilation in the future, which is the ultimate goal of this series of works.