Development of the long-range forecast system at Hydrometcentre of Russia in 2020

A brief description is given for the works carried out in 2020 to implement the longrange forecast technology based on the SLAV072L96 multiscale hydrodynamic atmosphere model. The purpose of these works was an improvement in simulating the deep convection and stratosphere dynamics. The works comprised the improvement and verification of the parameterizations for subgrid-scale processes and the whole model using long-range forecasts computed from historical initial data. As a result, the model correctly reproduces the main features of the annual mean precipitation field and zonal mean wind in the stratosphere. Keywords: long-range forecasts, global atmosphere model, parameterizations of subgrid-scale processes

Modelling extreme precipitation fields for large scale flood insurance

<p>Assessment of both localised and widespread flooding is vital for flood insurance to ensure adequate financial protection for businesses and property owners alike. But modelling precipitation and catchment response on very large spatial scales remains a challenge because of the availability of data and the high dimensionality of the problem. Modelling flood risk for insurance requires spatially coherent estimation of extremes which go beyond the historical record. At the national and continental scale, it can be difficult to apply models which maintain both the dependence structure of the precipitation field and the marginal distributions which determine local impacts. Recent research into spatiotemporal random fields modelling is highly promising. Numerical weather prediction is also an attractive prospect because correlations are implicitly captured in physical processes, but the computational demand and the uncertainty of perturbed physics ensembles can limit its usefulness.  </p><p>We introduce a data driven approach for widescale flood risk assessment based on modelling extreme precipitation fields. Using gridded reanalysis precipitation data, we identify extreme precipitation events in space and time using a measure of correlation in the tails of the marginal distributions. The simulation of extreme precipitation follows two main processes. First, the timing and extent of events are modelled using a Poisson distribution for event triggers, and a spatial Poisson process perturbs event footprints for observed events in the neighbourhood of the trigger location. The second stage is to model the extreme precipitation field within the event footprint. A Copula process is used to estimate extreme precipitation quantiles for all simulation points within the event ensuring internal spatial coherence. Our method has the flexibility to model extreme precipitation with any underlying physical conditions using computationally efficient models which facilitate widescale risk assessment.</p>

Westward-propagating Rossby modes in idealized GCMs

This work investigates the characteristics of westward-propagating Rossby modes in idealized global general circulation models. Using a nonlinear smoothing algorithm to estimate the background spectrum and an objective method to extract the spectral peaks, the 4 leading meridional modes can be identified for each of the first 3 zonal wavenumbers, with frequencies close to the predictions from the Hough modes obtained by linearizing about a state of rest. Variations in peak amplitude for different modes, both within a simulation and across simulations, may be understood under the assumption that the forcing of the modes scales with the background spectrum. Surface friction affects the amplitude and width of the peaks but both remain finite as friction goes to zero, which implies that some other mechanism, arguably nonlinear, must also contribute to the damping of the modes. Although spectral peaks are also observed for the precipitation field with idealized moist physics, there is no evidence of mode enhancement by the convective heating. Subject to the same friction, the amplitude of the peaks are very similar in the dry and moist models when both are normalized by the background spectra.

SHARPEN: A Scheme to Restore the Distribution of Averaged Precipitation Fields

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Role of forests in maintaining a water balance in the territory of Belarus

The article analyzes the relationship between the forest cover and the amount of summer rainfalls in Belarus. We found that the spatial structure of the long-term precipitation field in Belarus is largely explained by the spatial features of its forest cover. In particular, the high forest cover in summer time provides 5–15 % more rain falls than that without forest. We also showed that the extremely dry period from 2014 to 2015 led to a significant transformation of the summer rainfall field. As a result, the field becomes almost the mirror opposite to the summer air temperature field. This indicates the important role of local evaporation in the formation of precipitation in the summer months. The important conclusion of the results is that additional forest stands are needed to prevent a further decrease in the level of surface and ground waters in Belarus. We also need to increase the use of agroforestry techniques in crop cultivation.

Evaluation of CMIP6 Model Precipitation Variability Through Compositing in Cloud-defined Weather States

<p>In order to understand the mechanisms determining precipitation variability and to evaluate model skill in simulating those mechanisms, it is important to partition the precipitation field into regimes that include distinct sets of processes. In the past, dynamic fields like omega and SLP have been used to define regimes and study cloud, radiation, and precipitation variability. More recently, cloud-defined weather states were derived and used for similar analyses. Here, we apply a new cloud-defined Weather State dataset derived from the higher-resolution ISCCP-H data to examine precipitation variability at global scales and evaluate CMIP6 model precipitation simulations . In addition, precipitation partitioning using mid-tropospheric vertical velocity is performed, and the differences between the results of the two compositing methodologies are discussed.</p>

Using Commercial Microwave Links for a stochastic reconstruction of precipitation field ensembles

<p><span>Precipitation is one of the crucial variables within the hydrological system, and accordingly one of the main drivers for terrestrial hydrological processes. The quality of many hydrological applications such as climate prediction, water resource management, and flood forecasting, depends on the correct reproduction of its spatiotemporal distribution. However, the global network of precipitation observations is relatively sparse in large areas of the world. Compared to these observation network, inhabited areas typically have a relative dense network of Commercial Microwave Links (CMLs). These CMLs can be used to calculate path-averaged rain rates, derived from their attenuation. One challenge when using path-averaged rain rates is the construction of spatial precipitation fields. To address these challenges, we apply Random Mixing Whittaker-Shannon (RMWSPy) to stochastically simulate precipitation fields. Therefore, we generate precipitation fields as a linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized in such a way that the observations and the spatial structure of the precipitation observations are reproduced. Within this method the path-averaged rain rates are used as non-linear constrains. One big advantage when using RMWSPy is the ability to simulate precipitation field ensembles of any size, where each ensemble member is in concordance with the underlying observations. The spread of such an ensemble enables an uncertainty estimation of the simulated fields. In particular, it reflects the precipitation variability along the CML path and the uncertainty between the observation locations. We demonstrate RMWSPy using CML observations within various areas of Germany with a different density of observations. We show, that the reconstructed precipitation fields reproduce the observed spatial precipitation pattern in a comparable good quality as the RADOLAN weather radar data set provided by the German Weather Service (DWD).</span></p>

Assessing the viability of using GEOS-Forecast Product for Landslides Forecasting−A step toward Early Warning Systems

<p>Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time.</p><p>Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system.</p><p>This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS).  GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.</p>

Dynamics of drainage under stochastic rainfall in river networks

We consider a linearized dynamical system modeling the flow rate of water along the rivers and hillslopes of an arbitrary watershed. The system is perturbed by a random rainfall in the form of a compound Poisson process. The model describes the evolution, at daily time scales, of an interconnected network of linear reservoirs and takes into account the differences in flow celerity between hillslopes and streams as well as their spatial variation. The resulting stochastic process is a piece-wise deterministic Markov process of the Orstein–Uhlembeck type. We provide an explicit formula for the Laplace transform of the invariant density of streamflow in terms of the geophysical parameters of the river network and the statistical properties of the precipitation field. As an application, we include novel formulas for the invariant moments of the streamflow at the watershed’s outlet, as well as the asymptotic behavior of extreme discharge events, and conditions for the statistical scaling of streamflow with respect to Horton order.