Effect of large-scale coherent structures on turbulent convection

We study an effect of large-scale coherent structures on global properties of turbulent convection in laboratory experiments in air flow in a rectangular chamber with aspect ratios $A \approx 2$ and $A\approx 4$ (with the Rayleigh numbers varying in the range from $5 \times 10^6$ to $10^8$). The large-scale coherent structures comprise the one-cell and two-cell flow patterns. We found that a main contribution to the turbulence kinetic energy production in turbulent convection with large-scale coherent structures is due to the non-uniform large-scale motions. Turbulence in large Rayleigh number convection with coherent structures is produced by shear, rather than by buoyancy. We determined the scalings of global parameters (e.g., the production and dissipation of turbulent kinetic energy, the turbulent velocity and integral turbulent scale, the large-scale shear, etc.) of turbulent convection versus the temperature difference between the bottom and the top walls of the chamber. These scalings are in an agreement with our theoretical predictions. We demonstrated that the degree of inhomogeneity of the turbulent convection with large-scale coherent structures is small.Comment: 10 pages, 12 figures, REVTEX

Improved multi‐model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical‐statistical approach during fall and winter seasons

Skillful seasonal climate forecasts can support decision making in water resources management and agricultural planning. In arid and semi-arid regions, tailoring reliable forecasts has the potential to improve water management by using key hydroclimate variables months in advance. This article analyses and compares the performance of two common approaches (empirical and hybrid dynamical-statistical) in seasonal climate forecasting over a drought-prone area located in Southwest Asia including Iran. Empirical models are framed as a baseline skill that hybrid models need to outperform. Both approaches provide probabilistic forecasts of precipitation and temperature using canonical correlation analysis to provide forecasts at 0.25° resolution. Empirical models are developed based on the large-scale observed atmosphere–ocean patterns for forecasting using antecedent climate anomalies as predictors, while the hybrid approach makes use of model output statistics to correct systematic errors in dynamical climate model forecast outputs. Eight state-of-the-art dynamical models from the North American Multi-Model Ensemble project are analysed. Individual models with the highest goodness index are weighted to develop seven different hybrid dynamical-statistical Multi-model Ensembles. In this study, (October–December) and (January–February) are considered as target seasons which are the most important periods within the water year for water resource allocation to the agriculture sector. The results show that the hybrid approach has improved performance compared to the raw general circulation models and purely empirical models, and that the performance of the hybrid models is season-dependent. Seasonal forecasts of precipitation (temperature) have a higher skill in OND (JFM). In addition, in most cases, Multi-model Ensemble (MME) is more skillful than the empirical models and outperforms individual dynamical models. However, the best individual model might be as skillful as the MME given the target season and region of interest

Validation of TRMM 3B42 V6 for estimation of mean annual rainfall over ungauged area in semiarid climate

This research compares data from the Tropical Rainfall Measuring Mission 3B42 V6 with data obtained from 19 synoptic rain gauges during the period 1998–2010 over the semiarid climate of Khorasan Razavi, Iran. Validation was performed using three spatial extents, including 1 TRMM grid face from the synoptic station (1PTRM), 3 TRMM points surrounding the synoptic station (3PTRM) and 5 TRMM points surrounding the synoptic station (5PTRM), using ArcGIS 10.2 software. The perfect and poor r were obtained at stations S08 and S19, with values of 0.92 and 0.26, respectively. According to the Nash–Sutcliffe efficiency coefficient, the TRMM satellite can predict the spatial variation of the mean annual rainfall by 0.23, 0.43 and 0.38 for 1PTRM, 3PTRM and 5PTRM, respectively, at 19 stations. The agreement significantly increases by 0.88, 0.83 and 0.80 for 1PTRM, 3PTRM and 5PTRM, respectively, when gauges S05, S07, S11 and S13 are excluded from the dataset, which may be associated with orographic or instrumental error at the stations

Drought risk management using satellite-based rainfall estimates

In this chapter, we present an overview of the role of satellite-based rainfall estimates (SREs) in drought risk management applications, ranging from simple anomaly and index-based approaches to cross-cutting drought early warning systems (EWS) and financial instruments such as weather index-based insurance (WII) schemes. We contend that meteorological, hydrological, agricultural, and socioeconomic are aspects – not types – of drought, and a universally acceptable drought definition is not a prerequisite for the effective and efficient assessment of the impacts of drought using SREs and other satellite-based datasets and/or models. This is illustrated through examples from the work of the co-authors, as well as the wider community. The chapter concludes with a synthesis of the challenges for SREs and the current trends in the development and application of SREs in drought risk management, including an outlook of the priorities for future research and applications