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Matthias Steiner

Abstract

A new relationship has been established linking the vertical mean Doppler velocity of raindrop spectra and the accompanying differential reflectivities. It is based upon the specific radar combination of a vertically pointing Doppler and a polarization radar scanning at low elevations, and was derived from consideration of two extensive disdrometer datasets containing 40 000 raindrop-size distributions. Following a detailed error analysis of the new relation, the discussion is directed towards its possible application and limitation. It is shown that under optimum conditions the velocity errors are as low as 0.3 m s−1. This results in Doppler radar-derived drop-size distributions having liquid-water contents with uncertainties of only about 40%. Compared to standard particle fall speed estimations of the Rogers' type, this means an improvement in accuracy of more than a factor of three. Although the new technique is not suitable for operational use, it can provide fresh quantitative insight into the vertical structure of the dynamics and the microphysical processes of precipitation.

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Matthias Steiner
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Matthias Steiner and James A. Smith

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This study aims at assessing the potential of anomalous propagation conditions to occur, reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. Based on a 16-yr record of operational sounding data, the likelihood of atmospheric conditions to occur across the United States that potentially lead to anomalous propagation of radar signals is estimated. Anomalous signal propagation may lead to a significant contamination of radar data from ground echoes normally not seen by the radar, which could result in serious rainfall overestimates, if not recognized and treated appropriately. Many different approaches have been proposed to eliminate the problem of regular ground clutter close to the radar and temporary clutter resulting from anomalous signal propagation. None of the reported approaches, however, satisfactorily succeeds in the case of anomalous propagation ground returns embedded in precipitation echoes, a problem that remains a challenge today for radar data quality control. Taking strengths and weaknesses of past approaches into consideration, a new automated procedure has been developed that makes use of the three-dimensional reflectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated by means of a decision tree. The new algorithm appears to work equally well in situations where anomalous propagation ground returns are either separated from or embedded within precipitation echoes. Moreover, sea clutter echoes are identified as not raining and successfully removed.

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Matthias Steiner and James A. Smith

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The relationships between radar reflectivity factor Z, rainfall rate R, and rainfall kinetic energy flux E were analyzed based on a multiyear raindrop spectra dataset recorded by a Joss–Waldvogel disdrometer in the Goodwin Creek research watershed in northern Mississippi. Particular attention was given to the climatological variability of the relationships and the uncertainty by which one rainfall parameter may be estimated from another. Substantial variability for the coefficients of a power-law relationship Y = A b X b between two rainfall parameters Y and X (where Y and X may stand for any paired combination of Z, R, and E) was found. The variability of the exponent b, however, was small enough to support approaches of climatologically fixed exponents to simplify radar rainfall estimation procedures. The multiplicative factor A b should typically be adjusted on a storm basis. The uncertainty of the estimation of one rainfall parameter from another, being a function of the difference in weighting of the drop size by the two parameters and the variability of raindrop spectra, was found to be approximately 50% for the Z–R relation, 40% for the E–R relation, and 25% for the Z–E relation. For extreme precipitation intensities (R ≥ 100 mm h−1), this drop spectra–based uncertainty reduced to approximately 20% for all three relationships. The results exhibited significant sensitivity to the choice of method applied to determine the relationship between two rainfall parameters. Appreciable sensitivity of the relationship between rainfall parameters (i.e., power-law coefficients and drop spectra–based uncertainty) to the number of raindrops registered per 1-min drop spectrum was also found.

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Matthias Steiner and James A. Smith

Abstract

Scale differences may introduce a bias when comparing, merging, or assimilating rainfall measurements because the dynamic range of values representing the underlying physical process strongly depends on the resolution of the data. The present study addresses this issue from the perspective of how well coarser-resolution radar-rainfall observations may be used for evaluation of hydrologic point processes occurring at the land surface, such as rainfall erosion, infiltration, ponding, and runoff. Conceptual and quantitative analyses reveal that scale differences may yield substantial biases. Even for perfect measurements, the overall bias is composed of two contributing factors: one related to a reduction of dynamic range of rain rates and the other related to a dependence of the relationship between observed radar reflectivity factor and retrieved rainfall rate on the scale of observation. The effects of scale differences are evaluated empirically from a perspective of averaging in time based on raindrop spectra observations. Averaging drop spectra over 5 min, on average over a large dataset, resulted in an underestimation of median and maximum rainfall rates of approximately 50% compared to the corresponding 1-min values. Overall, standard deviations of rain rates retrieved from 5-min-averaged radar reflectivity factors may easily be off a corresponding high-resolution (1 min) rainfall rate by a factor 2 or more. This magnitude is larger than the uncertainty resulting from limitations of the radar measurement precision. Scale-difference effects are thus important and should be considered when comparing, merging, or assimilating data from very different spatial and temporal scales. A similar challenge arises for downscaling schemes attempting to recover subgrid-scale features from coarse-resolution information.

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Kyoko Ikeda, Matthias Steiner, and Gregory Thompson

Abstract

Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer.

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Remko Uijlenhoet, James A. Smith, and Matthias Steiner

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The controls on the variability of raindrop size distributions in extreme rainfall and the associated radar reflectivity–rain rate relationships are studied using a scaling-law formalism for the description of raindrop size distributions and their properties. This scaling-law formalism enables a separation of the effects of changes in the scale of the raindrop size distribution from those in its shape. Parameters controlling the scale and shape of the scaled raindrop size distribution may be related to the microphysical processes generating extreme rainfall. A global scaling analysis of raindrop size distributions corresponding to rain rates exceeding 100 mm h−1, collected during the 1950s with the Illinois State Water Survey raindrop camera in Miami, Florida, reveals that extreme rain rates tend to be associated with conditions in which the variability of the raindrop size distribution is strongly number controlled (i.e., characteristic drop sizes are roughly constant). This means that changes in properties of raindrop size distributions in extreme rainfall are largely produced by varying raindrop concentrations. As a result, rainfall integral variables (such as radar reflectivity and rain rate) are roughly proportional to each other, which is consistent with the concept of the so-called equilibrium raindrop size distribution and has profound implications for radar measurement of extreme rainfall. A time series analysis for two contrasting extreme rainfall events supports the hypothesis that the variability of raindrop size distributions for extreme rain rates is strongly number controlled. However, this analysis also reveals that the actual shapes of the (measured and scaled) spectra may differ significantly from storm to storm. This implies that the exponents of power-law radar reflectivity–rain rate relationships may be similar, and close to unity, for different extreme rainfall events, but their prefactors may differ substantially. Consequently, there is no unique radar reflectivity–rain rate relationship for extreme rain rates, but the variability is essentially reduced to one free parameter (i.e., the prefactor). It is suggested that this free parameter may be estimated on the basis of differential reflectivity measurements in extreme rainfall.

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Matthias Steiner and Robert A. Houze Jr.

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This study investigates the sensitivity of the estimated monthly convective rain fraction—that is, the percentage of the areal rain accumulation contributed by precipitation identified as convective—to variations of the Z–R parameters used in radar-based rainfall estimation. Accurate knowledge of the fractions of precipitation that are convective and stratiform is important for climatological studies estimating the heating of the atmosphere. Extensive datasets from two climatologically different precipitation regimes, Darwin, Australia, and Melbourne, Florida, are used. The potential uncertainty of using (i) an arbitrary choice of the power factor b and (ii) either single or multiple Z–R relations (stratified by precipitation type) for converting radar reflectivity to rain rate is investigated quantitatively.

The analyses reveal that estimates of the monthly convective rain fraction are sensitive to the choice of Z–R parameters. A maximum sensitivity is found for precipitation regimes with an approximately equal mix of rainfall from convective and stratiform precipitation systems. For example, estimates of the convective rain fraction for monsoonal rainfall at Darwin may range from 30% to 80%, solely depending on the choice of Z–R parameters, even though all of these Z–R relations are tuned to produce the same total rainfall. In contrast, for the highly convective, sea-breeze-triggered, multicellular storms around Melbourne, the estimates of the convective rain fraction may range from 80% to 100%.

Different approaches to how the appropriate parameters of the Z–R relation(s) may be obtained are discussed. Varying the Z–R parameters to maximize the correlation of the radar-estimated monthly rainfall at the gauge sites and the rain gauge accumulations does not reveal enough sensitivity to make any choice significantly better than a single Z–R relation for both convective and stratiform rain. Multiple Z–R relations may be justified, but apparently not on the basis of a convective–stratiform separation.

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Matthias Steiner, James A. Smith, and Remko Uijlenhoet

Abstract

The microphysical aspects of the relationship between radar reflectivity Z and rainfall rate R are examined. Various concepts discussed in the literature are integrated into a coherent analytical framework and discussed with a focus on the interpretability of ZR relations from a microphysical point of view. The forward problem of analytically characterizing the ZR relationship based on exponential, gamma, and monodisperse raindrop size distributions is highlighted as well as the inverse problem of a microphysical interpretation of empirically obtained ZR relation coefficients. Three special modes that a ZR relationship may attain are revealed, depending on whether the variability of the raindrop size distribution is governed by variations of drop number density, drop size, or a coordinated combination thereof with constant ratio of mean drop size and number density. A rain parameter diagram is presented that assists in diagnosing these microphysical modes. The number-controlled case results in linear ZR relations that have been observed for steady and statistically homogeneous or equilibrium rainfall conditions. Most rainfall situations, however, exhibit a variability of drop spectra that is facilitated by a mix of variations of drop size and number density, which results in the well-known power-law ZR relationships. Significant uncertainties are found to be associated with the retrieval of microphysical information from the ZR relation coefficients, but even more so with shortcomings of the measurement of rainfall information and the subsequent processing of that data to obtain a ZR relation. Given a proper consideration of the uncertainties, however, valuable microphysical information may be obtained, particularly as a result of long- term monitoring of rainfall for fixed observational settings but also through comparisons among different climatic rainfall regimes.

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