Multifractal characterization of rain fields with a strong orographic influence

Harris, Daniel; Menabde, Merab; Seed, Alan; Austin, G. L.

doi:10.1029/96jd01656

Cited by 121 publications

(147 citation statements)

References 15 publications

Supporting

Mentioning

134

Contrasting

Unclassified

Order By: Relevance

“…Extending this study to include stratiform as well as other convective systems would provide useful results as stratiform precipitating clouds are radiometrically very different from convective systems owing to differences in their vertical structure. On the basis of studies such as that of Harris et al [1996], the scaling structure and therefore the downscaling parameters would also differ greatly for stratiform precipitation.…”

Section: Discussionmentioning

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Concerning the presence of scaling and applicability of cascade models to temporal rainfall, this has been supported in a number of empirical data analyses (e.g. Hubert et al, 1993;Olsson, 1995;Harris et al, 1996;Svensson et al, 1996;Tessier et al, 1996). With regard to temporal rainfall modelling, this has been performed mainly by calibrating the general so-called universal multifractal model (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Güntner¹,

Olsson

Calver

et al. 2001

Hydrol. Earth Syst. Sci.

119

123

View full text Add to dashboard Cite

Rainfall data of high temporal resolution are required in a multitude of hydrological applications. In the present paper, a temporal rainfall disaggregation model is applied to convert daily time series into an hourly resolution. The model is based on the principles of random multiplicative cascade processes. Its parameters are dependent on (1) the volume and (2) the position in the rainfall sequence of the time interval with rainfall to be disaggregated. The aim is to compare parameters and performance of the model between two contrasting climates with different rainfall generating mechanisms, a semi-arid tropical (Brazil) and a temperate (United Kingdom) climate. In the range of time scales studied, the scale-invariant assumptions of the model are approximately equally well fulfilled for both climates. The model parameters differ distinctly between climates, reflecting the dominance of convective processes in the Brazilian rainfall and of advective processes associated with frontal passages in the British rainfall. In the British case, the parameters exhibit a slight seasonal variation consistent with the higher frequency of convection during summer. When applied for disaggregation, the model reproduces a range of hourly rainfall characteristics with a high accuracy in both climates. However, the overall model performance is somewhat better for the semi-arid tropical rainfall. In particular, extreme rainfall in the UK is overestimated whereas extreme rainfall in Brazil is well reproduced. Transferability of parameters in time is associated with larger uncertainty in the semi-arid climate due to its higher interannual variability and lower percentage of rainy intervals. For parameter transferability in space, no restrictions are found between the Brazilian stations whereas in the UK regional differences are more pronounced. The overall high accuracy of disaggregated data supports the potential usefulness of the model in hydrological applications.

show abstract

“…It has been observed (see, e.g., Harris et al ., 1996Harris et al ., , 2001Koh et al ., 2012 and references therein) that rain fields show in space and time a log-log linearity of the power spectrum, that is, E (k ) = k -β , where k = (k x 2 + k y 2 ) 1/2 with k x and k y the wavenumber components. Hence, the soobtained sloping scale β can be used as forecast smoothness indicator -more energy in the mean and large scales, or of its spikiness, more energy at the small scale (Davis et al ., 1996).…”

Section: Exp-(d)mentioning

confidence: 59%

Factors affecting the quality of QPF: a multi‐method verification of multi‐configuration BOLAM reforecasts against MAP D‐PHASE observations

Casaioli

Mariani

Malguzzi

et al. 2013

Meteorological Applications

View full text Add to dashboard Cite

A 6 month reforecast campaign is performed in order to produce, for each compared configuration, the corresponding forecast series. Results show that decreasing model grid spacing and simultaneously increasing model domain extension is effective in improving the QPF quality when higher-resolution initial and boundary conditions are directly applied to BOLAM, without using a coarser-resolution parent model run. Therefore, such skilful configuration has been deployed in late 2012 within the international research programme 'HYdrological cycle in Mediterranean EXperiment' (HyMeX). On the contrary, when keeping the low-resolution, double nesting configuration, improving input data decreases the QPF performance.

show abstract

Multifractal characterization of rain fields with a strong orographic influence

Cited by 121 publications

References 15 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

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Factors affecting the quality of QPF: a multi‐method verification of multi‐configuration BOLAM reforecasts against MAP D‐PHASE observations

Contact Info

Product

Resources

About