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- Author or Editor: T. B. Smith x
- Journal of Applied Meteorology and Climatology x
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Abstract
Physical storm characteristics during the operational period of the Santa Barbara Cloud Seeding Project have been studied. It is shown that the vertical storm structure, particularly the depth of the low-level convective layer, is of importance in determining (1) the area distribution of precipitation, (2) the transport of seeding material from the ground to the nucleation levels, and (3) the existence of supercooled liquid water at nucleation levels. Item (1) above influences the correlation between target and control precipitation amounts, and items (2) and (3) influence the effective seeding of the storm. The seeded and unseeded storms of the Project have been treated using these concepts in order to investigate their influence on the inconclusive statistical results of the Project.
On the basis of qualitative seedability criteria, it is estimated that approximately one-half of the precipitation in the Project period occurred under relatively poor seeding conditions. This was determined by classifying storms into convective and stable flow types. It is also shown that the convective and stable cases have differing orographic precipitation characteristics and that, as a consequence, the target-control relationship is a function of vertical storm stability.
The study suggests that the possibility of detecting seeding effects can be improved by elimination of poor seeding cases through development of better seedability critera and by stratifying target-control relationships according to storm type. Also indicated is a need for improved understanding of natural rainfall variations before substantial progress can be made in detecting detailed variations caused by seeding.
Abstract
Physical storm characteristics during the operational period of the Santa Barbara Cloud Seeding Project have been studied. It is shown that the vertical storm structure, particularly the depth of the low-level convective layer, is of importance in determining (1) the area distribution of precipitation, (2) the transport of seeding material from the ground to the nucleation levels, and (3) the existence of supercooled liquid water at nucleation levels. Item (1) above influences the correlation between target and control precipitation amounts, and items (2) and (3) influence the effective seeding of the storm. The seeded and unseeded storms of the Project have been treated using these concepts in order to investigate their influence on the inconclusive statistical results of the Project.
On the basis of qualitative seedability criteria, it is estimated that approximately one-half of the precipitation in the Project period occurred under relatively poor seeding conditions. This was determined by classifying storms into convective and stable flow types. It is also shown that the convective and stable cases have differing orographic precipitation characteristics and that, as a consequence, the target-control relationship is a function of vertical storm stability.
The study suggests that the possibility of detecting seeding effects can be improved by elimination of poor seeding cases through development of better seedability critera and by stratifying target-control relationships according to storm type. Also indicated is a need for improved understanding of natural rainfall variations before substantial progress can be made in detecting detailed variations caused by seeding.
Abstract
Recent developments to the Local-scale Urban Meteorological Parameterization Scheme (LUMPS), a simple model able to simulate the urban energy balance, are presented. The major development is the coupling of LUMPS to the Net All-Wave Radiation Parameterization (NARP). Other enhancements include that the model now accounts for the changing availability of water at the surface, seasonal variations of active vegetation, and the anthropogenic heat flux, while maintaining the need for only commonly available meteorological observations and basic surface characteristics. The incoming component of the longwave radiation (L↓) in NARP is improved through a simple relation derived using cloud cover observations from a ceilometer collected in central London, England. The new L↓ formulation is evaluated with two independent multiyear datasets (Łódź, Poland, and Baltimore, Maryland) and compared with alternatives that include the original NARP and a simpler one using the National Climatic Data Center cloud observation database as input. The performance for the surface energy balance fluxes is assessed using a 2-yr dataset (Łódź). Results have an overall RMSE < 34 W m−2 for all surface energy balance fluxes over the 2-yr period when using L↓ as forcing, and RMSE < 43 W m−2 for all seasons in 2002 with all other options implemented to model L↓.
Abstract
Recent developments to the Local-scale Urban Meteorological Parameterization Scheme (LUMPS), a simple model able to simulate the urban energy balance, are presented. The major development is the coupling of LUMPS to the Net All-Wave Radiation Parameterization (NARP). Other enhancements include that the model now accounts for the changing availability of water at the surface, seasonal variations of active vegetation, and the anthropogenic heat flux, while maintaining the need for only commonly available meteorological observations and basic surface characteristics. The incoming component of the longwave radiation (L↓) in NARP is improved through a simple relation derived using cloud cover observations from a ceilometer collected in central London, England. The new L↓ formulation is evaluated with two independent multiyear datasets (Łódź, Poland, and Baltimore, Maryland) and compared with alternatives that include the original NARP and a simpler one using the National Climatic Data Center cloud observation database as input. The performance for the surface energy balance fluxes is assessed using a 2-yr dataset (Łódź). Results have an overall RMSE < 34 W m−2 for all surface energy balance fluxes over the 2-yr period when using L↓ as forcing, and RMSE < 43 W m−2 for all seasons in 2002 with all other options implemented to model L↓.
Abstract
A historical water clarity index (K d index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (K d ; m−1) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.
Abstract
A historical water clarity index (K d index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (K d ; m−1) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.
Abstract
The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dBZ) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).
Abstract
The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dBZ) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).
