Konstantin Belyaev

Changes in the Duration of European Wet and Dry Spells during the Last 60 Years

AbstractDaily rain gauge data over Europe for the period from 1950 to 2009 were used to analyze changes in the duration of wet and dry spells. The duration of wet spells exhibits a statistically significant growth over northern Europe and central European Russia, which is especially pronounced in winter when the mean duration of wet periods increased by 15%–20%. In summer wet spells become shorter over Scandinavia and northern Russia. The duration of dry spells decreases over Scandinavia and southern Europe in both winter and summer. For the discrimination between the roles of a changing number of wet days and of a regrouping of wet and dry days for the duration of the period, the authors suggest a fractional truncated geometric distribution. The changing numbers of wet days cannot explain the long-term variability in the duration of wet and dry periods. The observed changes are mainly due to the regrouping of wet and dry days. The tendencies in duration of wet and dry spells have been analyzed for a numb...

Improving Estimates of Heavy and Extreme Precipitation Using Daily Records from European Rain Gauges

Abstract The long-term variability in heavy precipitation characteristics over Europe for the period 1950–2000 is analyzed using high-quality daily records of rain gauge measurements from the European Climate Assessment (ECA) dataset. To improve the accuracy of heavy precipitation estimates, the authors suggest estimating the fractional contribution of very wet days to total precipitation from the probability distribution of daily precipitation than from the raw data, as it is adopted for the widely used R95tot precipitation index. This is feasible under the assumption that daily precipitation follows an analytical distribution like the gamma probability density function (PDF). The extended index R95tt based on the gamma PDF is compared to the classical R95tot index. The authors find that R95tt is more stable, especially when precipitation extremes are estimated from the limited number of wet days of seasonal and monthly time series. When annual daily time series are analyzed, linear trends in R95tt and R...

Probability Distribution Characteristics for Surface Air–Sea Turbulent Heat Fluxes over the Global Ocean

To analyze the probability density distributions of surface turbulent heat fluxes, the authors apply the twoparametric modified Fisher–Tippett (MFT) distribution to the sensible and latent turbulent heat fluxes recomputed from 6-hourly NCEP–NCAR reanalysis state variables for the period from 1948 to 2008. They derived the mean climatology and seasonal cycle of the location and scale parameters of the MFT distribution. Analysis of the parameters of probability distributions identified the areas where similar surface turbulent fluxes are determined by the very different shape of probability density functions. Estimated extreme turbulent heat fluxes amount to 1500–2000 W m22 (for the 99th percentile) and can exceed 2000 W m22 for higher percentiles in the subpolar latitudes and western boundary current regions. Analysis of linear trends and interannual variability in the mean and extreme fluxes shows that the strongest trends in extreme fluxes (more than 15 W m22 decade21) in the western boundary current regions are associated with the changes in the shape of distribution. In many regions changes in extreme fluxes may be different from those for the mean fluxes at interannual and decadal time scales. The correlation between interannual variability of themean and extreme fluxes is relatively low in the tropics, the SouthernOcean, and the Kuroshio Extension region.Analysis of probability distributions in turbulent fluxes has also been used in assessing the impact of sampling errors in theVoluntaryObserving Ship (VOS)-based surface flux climatologies, allowed for the estimation of the impact of sampling in extreme fluxes. Although sampling does not have a visible systematic effect onmean fluxes, sampling uncertainties result in the underestimation of extreme flux values exceeding 100 W m22 in poorly sampled regions.

Statistical analysis of precipitation events

In the present paper we demonstrate the results of a statistical analysis of some characteristics of precipitation events and propose a kind of a theoretical explanation of the proposed models in terms of mixed Poisson and mixed exponential distributions based on the information-theoretical entropy reasoning. The proposed models can be also treated as the result of following the popular Bayesian approach.

The REMO Ocean Data Assimilation System into HYCOM (RODAS_H): General Description and Preliminary Results

Abstract The first version of the Brazilian Oceanographic Modeling and Observation Network (REMO) ocean data assimilation system into the Hybrid Coordinate Ocean Model (HYCOM) (RODAS_H) has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation (EnOI) scheme and considers the high frequency variability of the model error co-variance matrix. The EnOI can assimilate sea surface temperature (SST), satellite along-track and gridded sea level anomalies (SLA), and vertical profiles of temperature (T) and salinity (S) from Argo. The first observing system experiment was carried out over the Atlantic Ocean (78°S–50°N, 100°W–20°E) with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integrations were performed, including the control run without assimilation. In the other four, different observations were assimilated: SST only (A_SST); Argo T-S profiles only (A_Argo); along-track SLA only (A_SLA); and all data employed in the previous runs (A_All). The A_SST, A_Argo, and A_SLA runs were very effective in improving the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to observations. Overall, the A_All run produced a good analysis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.

An application of a data assimilation method based on the diffusion stochastic process theory using altimetry data in Atlantic

Abstract A data assimilation (DA) method based on the application of the diffusion stochastic process theory, particularly, of the Fokker-Planck equation, is considered. The method was introduced in the previous works; however, it is substantially modified and extended to the multivariate case in the current study. For the first time, the method is here applied to the assimilation of sea surface height anomalies (SSHA) into the Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean. The impact of assimilation of SSHA is investigated and compared with the assimilation by an Ensemble Optimal Interpolation method (EnOI). The time series of the analyses produced by both assimilation methods are evaluated against the results from a free model run without assimilation. This study shows that the proposed assimilation technique has some advantages in comparison with EnOI analysis. Particularly, it is shown that it provides slightly smaller error and is computationally efficient. The method may be applied to assimilate other data such as observed sea surface temperature and vertical profiles of temperature and salinity.

An extension of a data assimilation method based on the application of the Fokker–Planck equation

Abstract A data assimilation method based on the Kalman filter theory and on the Fokker–Planck equation is extended to assimilate Atlantic Ocean data into a new version of the well-known Modular Ocean Model (MOM_3) from NOAA/GFDL. This extension enables assimilation of non-uniformly distributed data in space and time. Numerical experiments with Levitus atlas data are carried out with the ocean model configured at a low resolution. Some results of these experiments as well as other possible expansions are discussed.

An optimal data assimilation method and its application to the numerical simulation of the ocean dynamics

ABSTRACT An original data assimilation (DA) scheme with a general dynamics model is considered. It is shown that this scheme can be approximated by the stochastic diffusion process. The sufficient conditions to provide this approximation are formulated. Based on this algorithm a new DA method is developed. The method combines variational and statistical approaches commonly used in DA theory and minimizes the variance of the trajectory of a diffusion process in conjunction with a dynamics numerical model. In this sense the method is optimal in contrast to other DA approaches. The proposed scheme takes the model dynamics into account and in this way it differs from the well-known Kalman filter. Furthermore, the derived DA method can be applied to a very wide field of dynamical systems, for example, gas dynamics, fluid dynamics and other disciplines. However, the current study deals with oceanography and DA in oceanography specifically. Then the method is applied to the HYbrid Coordinate Ocean Model and assimilates satellite sea level anomaly data from the Archiving, Validating and Interpolating Satellite Oceanography Data over the Atlantic Ocean to correct the model state. Several numerical experiments have been performed. The experiments show that the method substantially changes the synoptic and mesoscale structure of ocean dynamics. Also, the distribution of the obtained result is estimated through the solution of the Fokker–Planck–Kolmogorov equation.

Probability distribution for the visually observed fractional cloud cover over the ocean

AbstractThe authors suggest a three-parameter bounded distribution from the family of mixed gamma distributions for characterizing the probability density distributions of fractional total and low cloud cover over the global oceans. The authors derive both the continuous form of this distribution and its discrete counterpart, which can be directly applied to cloud cover reports. The distribution is applied to the cloud cover characteristics reported by voluntary observing ships (VOS) for the period from 1950 to 2011 after filtering nighttime observations with poor lunar illumination. The suggested distribution demonstrates a high goodness of fit to the data and good skill in capturing probability distributions with different shapes. The authors present seasonal climatologies of the parameters of the derived distribution for the chosen 60-yr period and demonstrate that applying the PDF-based concept to the analysis of cloud cover allows identification of areas where similar mean cloud amounts can be produc...

Statistical Modeling of Air-Sea Turbulent Heat Fluxes by Finite Mixtures of Gaussian Distributions

The approach originally developed for the investigation of the traffic, that is, the intensities of information flows in financial markets, is applied for the statistical analysis of climatic data. The statistical regularities in the behavior of sensible and latent turbulent heat fluxes recomputed from 6-hourly NCEP-NCAR for the period \(1948-2008\) in Atlantic are analyzed. It is proposed to represent these regularities by probability distributions that are mixtures of several normal (Gaussian) laws with parameters varying in time. The method of moving separation of mixtures is used to obtain the values of the parameters of the mixtures. This approach allows to analyze the regularities in the variation of the parameters and, hence, to capture the low-term variability which can be considered as a trend and high-term dynamics associated with diffusion or irregular variability.