Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions

Perica, Sanja; Foufoula‐Georgiou, Efi

doi:10.1029/96jd01870

Cited by 170 publications

(159 citation statements)

References 8 publications

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“…Second, at each iterative step (i.e., after step 4 above) the four new pixels generated within each of the larger pixels from the previous step (e.g., the four new pixels at 1.5 km resolution within a single 3 km pixel) are rearranged to improve connectivity. This is done by shifting the largest intensity to the pixel that is surrounded by high intensities and the lowest intensity to the pixel surrounded by the lowest intensities [Perica, 1995]. This rearranging is important as the four new pixels are uncorrelated to each other and to surrounding pixels.…”

Section: Fourier Power Spectrum and Scaling In Atmospheric Fieldsmentioning

confidence: 99%

Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval

Harris¹,

Foufoula‐Georgiou²

2001

J. Geophys. Res.

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Abstract. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)Goddard Profiling (GPROF) rainfall retrieval algorithm is an inversion type algorithm, which uses numerical cloud models and radiative transfer schemes to simulate the brightness temperatures that the TMI would see, thereby allowing one to relate hydrometeor profiles to brightness temperature. The variability in modeled hydrometeor fields is known to have an important effect on simulated brightness temperatures, and while the TMI instrument sees all the variability down to scales of a few meters, cloud models are typically run at resolutions of 1-3 km. This paper is an illustrative investigation into the importance of subgrid variability (scales below 1-3 km), which is ignored when simulating brightness temperatures. Previous studies on the importance of subgrid variability have been based on comparisons of simulated brightness temperatures computed from hydrometeor fields of a high resolution model and spatially aggregated hydrometeor fields from the same model run. It is argued that numerical cloud models have reduced small-scale variability due to model artifacts such as computational mixing, and this may lead to an underestimation of the importance of including subgrid variability. To address this problem, stochastic downscaling developed in a wavelet-based framework is used to reintroduce the variability reduced by computational mixing. In particular, a high resolution model is spatially aggregated (i.e., upscaled) over the scales affected by computational mixing and stochastically downscaled back to the original resolution of the model. The higher degree of variability introduced by the downscaling (which is a closer approximation to the variability observed in hydrometeor concentrations as compared to that produced by high resolution models) is found to result in larger biases in estimated brightness temperature. This points to the potential for a significant source of bias in microwave-sensed precipitation retrievals that requires further study.

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Section: Fourier Power Spectrum and Scaling In Atmospheric Fieldsmentioning

confidence: 99%

Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval

Harris¹,

Foufoula‐Georgiou²

2001

J. Geophys. Res.

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“…Olsson, 1998;Basu et al, 2004). Perica and Foufoula-Georgiou (1996) find that adding noise independent of the smallest resolved scale can lead to deviations in the spatial correlations of their rain fields. This may be a reason why Olsson (1998) explicitly makes his perturbations a function of the resolved precipitation beforehand and afterwards.…”

Section: Introductionmentioning

confidence: 99%

“…A version without using information about the subscale cloud fraction could be used for the downscaling of measured or modelled precipitation and radiation fields. Such an algorithm for 2D radar-derived rain fields would be similar to the one of Ferraris et al (2003) or Perica and Foufoula-Georgiou (1996). Their algorithms add multiplicative noise independently of the resolved field, leaving the coarse field visible in the final product.…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for the downscaling of cloud fields

Venema

Garc

Simmer

2010

Quart J Royal Meteoro Soc

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We present a novel algorithm for the downscaling of three-dimensional cloud fields. The goal of the algorithm is to add realistic subscale variability to a coarse field taking the resolved variability into account. The method is tested by coarse graining high-resolution sparse cumulus and broken stratocumulus clouds in the horizontal plane, downscaling these coarse fields back to the high resolution and comparing the radiative and microphysical properties of these downscaled fields with the original high-resolution fields. The resolutions of the cumulus and stratocumulus clouds used for this purpose are increased by a factor of four and ten, respectively. The downscaling decreases the errors in the flux transmittance and reflectance of the cumulus and stratocumulus cloud fields by at least a factor of ten and three, respectively, compared to utilising the coarse cloud fields. A novel aspect of our algorithm is the fact that it constrains the high-resolution fields of cloud liquid water content as well as the subscale cloud fraction. An alternative version that does not include cloud fraction information is less accurate, but still significantly better than using the coarse fields. The latter downscaling algorithm can also be utilised for the disaggregation of geophysical fields for which fractional coverages are not defined. Furthermore, the downscaling algorithm can be combined with our other algorithms to generate surrogate fields with other constraints, for example, surrogate clouds with a prescribed liquid water content height distribution.

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“…To carry out all the simulations described above, the following assumptions have been made: • The NEXRAD radar measurements are assumed to be representative of the true precipitation distribution; because rainfall exhibits considerable heterogeneity at smaller scales than those characterizing NEXRAD (4 km), the spatial resolution of this true precipitation has been increased to 1 km through a multiscale disaggregation method (Perica and Foufoula-Georgiou 1996). This method has the capability to statistically reproduce the rainfall variability at different scales and is conditioned on large-scale rainfall statistics.…”

Section: Assumptions and Simulationsmentioning

confidence: 99%

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

2014

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Retrieving precipitation data from a rain gauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e., network density and location of the rain gauges and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume. The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a rain gauge network influence the estimation of the hydrograph and its characteristics in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil-type characteristics. The TIN-based real-time integrated basin simulator (tRIBS) hydrologic model was used to simulate hydrologic response at Baron Fork Basin, Oklahoma. Downscaled next-generation radar (NEXRAD) measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from the interpolation of eight fictitious rain gauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics and then the real soil-type distribution. Results have shown the dependence of the best rain gauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution.

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Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions

Cited by 170 publications

References 8 publications

Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval

Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval

A new algorithm for the downscaling of cloud fields

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

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