Comparison of Freezing-Level Altitudes from the NCEP Reanalysis with TRMM Precipitation Radar Brightband Data

Harris, Gettys Nunn; Bowman, Kenneth P.; Shin, Dong‐Bin

doi:10.1175/1520-0442(2000)013<4137:coflaf>2.0.co;2

Cited by 64 publications

(58 citation statements)

References 13 publications

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“…500-700 m, except at latitudes higher than about 35 • . According to Harris et al (2000) the average difference for 1998 is around 600 m, while the average zonal difference ranges from 200 to 1000 m. The differences obtained from this study are very similar to the results reported by Harris et al (2000). The similarity between the BBH and NFL suggests that the BBH is closely related to the temperature variability.…”

Section: Differences Between the Proxy Variablessupporting

confidence: 87%

“…The NFL is determined from the monthly geopotential height of the 0 • C isotherm, which can be derived by an interpolation of the monthly air temperature profile at the 17 pressure levels. The algorithm used to obtain the NFL is based on a report by Harris et al (2000). The algorithm searches for zero crossings in the air temperature profile between 1000 and 200 hPa.…”

Section: Ncep Freezing-level Altitudementioning

confidence: 99%

“…The algorithm uses three quantities: the altitude of the maximum radar reflectivity, the altitude of the largest positive reflectivity gradient and the altitude of the largest negative reflectivity gradient. As a validation of the BBHs estimated by Shin et al (2000), Harris et al (2000) assessed the latitudinal and seasonal variations of the BBHs and the freezing levels directly computed from the National Centers for Environmental Prediction (NCEP) reanalysis data. The similarity between the freezing levels and the BBHs suggests that the NCEP freezing level data may be used as a proxy variable for the rain column height.…”

Section: Introductionmentioning

confidence: 99%

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Impact of proxy variables of the rain column height on monthly oceanic rainfall estimations from passive microwave sensors

Kim

Shin

Jin

2011

International Journal of Remote Sensing

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A major uncertainty in physically based algorithms that are used to estimate rainfall from passive microwave sensors arises from a lack of information on physical parameters such as the rain column height and the freezing level in rainy conditions. This uncertainty occurs because the rainfall integrated along a path on the rain column determines the relationship between the brightness temperature and the rainfall. The rain column height, however, is not well determined directly from simultaneous measurements. Most estimation models use the freezing level derived from an indirect method to obtain the unknown parameter. In this study, the characteristics of three variables that may be used as a proxy variable of the rain column height are investigated.The two variables are derived from the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and precipitation radar (TPR). They include the TMI-estimated freezing level (TFL) and the TPR-estimated bright-band height (BBH). The third variable is the freezing-level altitude derived from National Centers for Environmental Prediction (NCEP) reanalysis data (NCEP reanalysis freezing level (NFL)). Monthly oceanic rainfall estimations were then performed using the three aforementioned variables in place of the rain column height. As expected, the results show that differences in the rainfall estimates are greater in the regions where larger differences exist among the three variables. The analysis confirmed that an underestimate of the rain column height causes an overestimate of the rainfall. In addition, rainfalls that were underestimated with the BBH or NFL can be corrected with an empirical adjustment. This suggests that the TFL, BBH and NFL contain information related to the rain column height. However, the BBH and NFL require a correction in the mid-latitudes when their magnitude is low.

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Section: Differences Between the Proxy Variablessupporting

confidence: 87%

Section: Ncep Freezing-level Altitudementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of proxy variables of the rain column height on monthly oceanic rainfall estimations from passive microwave sensors

Kim

Shin

Jin

2011

International Journal of Remote Sensing

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“…Fu and Liu (2001) showed regional variability in the shape of the rain profile. Harris et al (2000), Berg et al (2002), and Ikai and Nakamura (2003) found biases in the freezinglevel estimates used in TMI retrievals, especially over midlatitude regions during winter. Bauer et al (1999), Olson et al (2001), and Battaglia et al (2003) investigated the overestimation of stratiform rain by passive microwave retrievals because of increased emission by melting hydrometeors in the bright band.…”

Section: Introductionmentioning

confidence: 93%

Rainfall Climate Regimes: The Relationship of Regional TRMM Rainfall Biases to the Environment

Berg

L’Ecuyer

Kummerow

2006

Journal of Applied Meteorology and Climatology

162

133

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Intercomparisons of satellite rainfall products have historically focused on the issue of global mean biases. Regional and temporal variations in these biases, however, are equally important for many climate applications. This has led to a critical examination of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Because of the time-dependent nature of these biases, it is not possible to apply corrections based on regionally defined characteristics. Instead, this paper seeks to relate PR-TMI differences to physical variables that can lead to a better understanding of the mechanisms responsible for the observed differences. To simplify the analysis, issues related to differences in rainfall detection and intensity are investigated separately. For clouds identified as raining by both sensors, differences in rainfall intensity are found to be highly correlated with column water vapor. Adjusting either TMI or PR rain rates based on this simple relationship, which is relatively invariant over both seasonal and interannual time scales, results in a 65%-75% reduction in the rms difference between seasonally averaged climate rainfall estimates. Differences in rainfall detection are most prominent along the midlatitude storm tracks, where widespread, isolated convection trailing frontal systems is often detected only by the higher-resolution PR. Conversely, over the East China Sea clouds below the ϳ18-dBZ PR rainfall detection threshold are frequently identified as raining by the TMI. Calculations based on in situ aerosol data collected south of Japan support a hypothesis that high concentrations of sulfate aerosols may contribute to abnormally high liquid water contents within nonprecipitating clouds in this region.

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“…Stewart et al (1984) presented a mean difference of 0.3 km in California (maritime system) for the stratiform precipitation. Several other studies (Leary and Houze, 1979;Harris et al, 2000;Thurai et al, 2003a,b) reported higher values and ranging from 0 to 0.9 km. This difference depends upon latitude, season and geography.…”

Section: Distribution Of Bright Band Heightmentioning

confidence: 82%

Observational study on melting layer characteristics over Palau in Pacific Ocean

Krishna

Reddy

Mastanaiah

et al. 2014

Journal of Atmospheric and Solar-Terrestrial Physics

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Comparison of Freezing-Level Altitudes from the NCEP Reanalysis with TRMM Precipitation Radar Brightband Data

Cited by 64 publications

References 13 publications

Impact of proxy variables of the rain column height on monthly oceanic rainfall estimations from passive microwave sensors

Impact of proxy variables of the rain column height on monthly oceanic rainfall estimations from passive microwave sensors

Rainfall Climate Regimes: The Relationship of Regional TRMM Rainfall Biases to the Environment

Observational study on melting layer characteristics over Palau in Pacific Ocean

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