Joint Session J3B.2A Determination of PBL Dynamics from Water Vapor Profile gradients and potential temperature

Tuesday, 3 August 2010: 9:15 AM
Torrey's Peak III & IV (Keystone Resort)
Chuen Meei Gan, City College of New York, New York, NY; and B. Gross and J. E. Gonzalez

Presentation PDF (253.0 kB)

There is a growing interest to determine Air Quality from combining satellite AOD with estimates of the Planetary Boundary Layer (PBL) height. The need to estimate PBL height is important since the freshly emitted aerosol particulates are trapped within this layer. Therefore, we can expect that AOD can be combined with MET model predictions of PBL layer heights  leading to ultimately improve PM2.5 estimates on a mesoscale level. In this presentation, we explore several MET based techniques for estimating PBL Height. The simplest cases we explore are the Potential Temperature Gradient and the Richardson Number threshold when comparing PBL heights from lidar systems with 1) measurements from nearby radiosonde and 2) mesoscale model s. 

When comparing PBL heights obtained by wavelet analysis of the lidar 1064nm backscatter profile, we find that the potential temperature gradient is more robust than the Richardson number approach with less false positives. On the other hand, we have also investigated the use of tracking the water vapor gradient.  In particular, the water vapor mixing ratio gradient can be a very accurate indicator of the maximum height of the aerosol pollution layer as demonstrated in figure 1

This result is also consistent with results obtained from radiosonde profiles where we found that by combining the potential temperature gradient with the water vapor mixing ratio gradient was in the majority of cases consistent and led to a unique PBL height estimate.

To study this in more detail, we first obtained 31-day coincident lidar observations with the CALIPSO passing over the NYC area. We compare the CALIPSO-derived PBL tops (using the wavelet analysis on CALIPSO level-1B (stage-3) attenuated backscatter profiles as in section 4) with colocated radiosonde measurements. Figure 2 shows their day-by-day comparison and correlation and quite good agreement is indicated with the correlation coefficient of 0.73. 

            On the other hand, for satellite applications, it is important to extend the MET estimation of PBL height from physical measurements from radiosondes with high vertical resolution to MET predictive models that are available in near real time. This presentation will further discuss the feasibility and consistency of MET based PBL estimators which will be compared against lidar derived values specifically using the Weather Research Forecast (WRF) Model using both conventional as well as urbanized parameterizations (i.e uWRF) when configured for NYC area. Applications of PBL estimations to Urban Heat Islands and PM2.5 estimation will also be discussed.    

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