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P2.55 Visibility Versus Precipitation Rate and Relative Humidity

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P2.55 Visibility Versus Precipitation Rate and Relative Humidity P2.55 VISIBILITY VERSUS PRECIPITATION RATE AND RELATIVE HUMIDITY I. Gultepe1,2, and G. A. Isaac1 1Cloud Physics and Severe Weather Research Section, Science and Technology Branch, Environment Canada Toronto, Ontario, M3H 5T4, Canada 1. INTRODUCTION 2. OBSERVATIONS The purpose of this work is to analyze the The main observations used in the analysis were relationships between visibility and precipitation the precipitation rates for rain and snow from the rate (PR), and visibility and relative humidity Desert Research Institute (DRI) manufactured with respect to water (RHw), obtained from hot plates (Rasmussen et al., 2002), the surface measurements. Precipitation Occurrence Sensor System (POSS) (Sheppard, 1990), and from the Visalia FD12P, Presently, visibility parameterizations related to ice and liquid particle characteristics from the precipitation type in forecast models are not optical probes, visibility from the Belfort adequate because they represent mid-latitude visibility meter, the Vaisala FD12P and from the cloud systems (Stoelinga, 1999). Some previous fog measuring device (FMD), and relative works have shown that particle phase is an humidity and temperature from the Campbell important factor in the visibility calculation but Scientific instruments. Details on these usually it is ignored (Rasmussen et al., 1999). instruments can be found in Isaac et al. (2005) The relative humidity with respect to water in and Gultepe et al. (2006). forecast models is used for visibility parameterization but changes have been 3. ANALYSIS AND RESULTS continuously made because of the uncertainty in The data were collected during the Alliance Icing accurate RHw measurements (Smirnova et al., Research Study (AIRS 2) which was conducted 2000). The earlier works suggested that in the Ottawa-Mirabel area from 3 November significant differences exist among the various 2003 to 12 February 2004 (Isaac et al., 2005) and parameterizations of visibility and that their during the Fog Remote Sensing And Modeling application to forecasting models should be (FRAM) field project that took place at the carefully addressed. Center for Atmospheric Research Experiment (CARE) site for the winter of 2005-2006 In this work, the visibility (vis) versus RHw (Gultepe et al., 2006). relationships were obtained using surface observations, and compared with those of the In this work, the vis values averaged over 1 Rapid Update Cycle (RUC) model. Also, vis minute intervals were plotted against RHw, and versus droplet number concentration (Nd) and ice PR for snow and rain. Also, the Nd and Ni particle number concentrations (Ni) were derived measurements from the FMD and York for non-precipitating boundary layer conditions, University ice particle counters (IPC, Savelyev et and it is suggested that both Nd and Ni should be al., 2003), respectively, were plotted against vis included in the fog parameterizations. Finally, that represented liquid and ice fog conditions. vis versus precipitation rate relationships, using The vis and PR measurements from the Vaisala previous studies, were evaluated to enable better probe were used in the analysis. The vis values predictions of vis from the forecast models. from the DRI hot plates and POSS were not used in the relationships for the FRAM project 2Corresponding author: Dr. Ismail Gultepe, because of the large variability in PR Cloud Physics and Severe Weather Research measurements. Section, Science and Technology Branch, Environment Canada, Toronto, Ontario, M3H a. Visibility versus RHw 5T4, Canada. Email:[email protected] The vis-RHw parameterizations used in the RUC model is preset such that vis reaches 5 km at 95% RHw. Fig. 1 shows the vis-RHw relationships used in the RUC model (Smirnova et al., 2000) and is given as − 31.1 visNd = 238N d . (5) where vis is given in km, Ni in number per liter, visRUC −= RHw − )80/)15(*5.2exp(60 . (1) and Nd in number per centimeter cubed. The vis values >50 km obtained from Eq. 5 should not be Using the vis and RHw surface observations considered significant because of uncertainties in obtained from the Toronto Pearson International the counting of particles at small sizes. Airport, the vis-RHw relationship obtained for hourly data is shown on Fig. 1 (black solid line). It should be noted that, based on the nature of Then, observations from the AIRS2 Mirabel site log-log plots, the variability in vis obtained from are overlaid on the plot for 1) all the data points, -1 Eq. 4 can be very large for a given Ni value; 2) T<-1°C, and 3) PR>0.1 mm h . A fit to data therefore, these results should be used representing snow conditions (#2) is also shown cautiously. in Fig. 1. The relationships for both vis values from the present work, representing the Pearson c. Vis versus PR from AIRS2 and FRAM Airport (FRAM) and AIRS2 data, are derived, The results from both the AIRS2 and FRAM respectively, as projects are presented here. Some of the earlier fits are also shown over the observed data points visFRAM −= RHw + 3.192)ln(5.41 , (2) in Fig. 3a. This figure shows the values from the hot plates and POSS for T<-1°C and T>-1°C, and respectively. Overall, two regions of data points are seen. It should be noted that there is no sharp 2 vis AIRS −= 0177.0 RHw + 462.1 RHw + 8.30 (3) separation between them but, when T>-1°C (mostly green points), vis values become larger where RHw is given between 30 and 100, as compared to those of T<-1°C that represents representing percentage values. Differences the snow conditions. Overall, a trend for snow among the 3 fits given above are found to be conditions exists with decreasing vis for higher precipitation rates. Fig. 3b shows a similar plot very distinct. In general, visRUC near 100% RHw results in about 2 times larger than vis values but it uses only the T criteria and the fits from obtained from the other data sets. Note that total previous studies. The two solid black lines visibility in a numerical model is obtained using indicate the regions for snow and wet snow both vis-RHw and the vis-PR relationships. In conditions based on relationships given by the RUC model, for snow and rain visibility Rasmussen et al. (1999). The variability is large calculations, the parameterizations given by and none of the earlier fits represent the results Stallabrass (1985) and Kunkel (1984) have been entirely. This indicates that some other used, respectively. parameters need to be considered for vis-PR relationships. b) Visibility versus Nd and Ni Visibility versus particle number concentration An experimental test bed site at CARE during for a fog type (e.g. particle phase) has always FRAM was setup as a part of Airport Vicinity been ignored in forecast models (Gultepe et al., and Icing Studies Applications (AVISA) project 2006; Stoelinga et al., 1999). Using the state of (Isaac et al., 2006). Based on the availability of both vis and PR from the VAISALA FD12P, a art observations of Nd and Ni from the new V optical probes, e.g. Droplet Measurement relationship between vis and PRV is obtained. Technologies (DMT) FMD and York University Fig. 4 shows that a trend exists when T<-1°C, IPC (Gultepe et al., 2006; and Savelyev et al., representing snow conditions. Some 2003), correspondingly), vis versus Ni, and vis accumulation of data points just above the data versus Nd together with their fits are shown in points, representing snow conditions, shows that Fig. 2a and 2b, respectively. These relationships wet snow conditions were present similar to the are obtained as AIRS2 data (Fig. 3), but this needs to be further researched. It should also be stated that PR less -1 − 56.0 than 0.1 mm h are not reliable due to sampling = 18Nvis , (4) Ni i issues. and 4. DISCUSSION AND CONCLUSIONS Technical support for the data collection was In the present work, surface observations from provided by the Cloud Physics and Severe the various instruments, measuring Nd, Ni, PR, Weather Research Section of the Science and vis, RHw, and T, were used in the analysis. The Technology Branch, Environment Canada, relationships between vis and a related parameter Toronto, Ontario. Authors were also thankful to (e.g. PR) were evaluated and compared with M. Wasey and R. Reed of Environment Canada previous works. Based on the results, the for technical support during the FRAM. following conclusions can be drawn: • The use of the previous relationships in 6. REFERENCES the forecast models especially for RHw cannot Gultepe, I., S.G. Cober1, G. A.Isaac1, D. Hudak, be acceptable because of an underestimation of P. King1, P. Taylor, M. Gordon, P. Rodriguez, B. vis at values close to saturation. Eq. 3 can be Hansen1, and M. Jacob, 2006: The Fog Remote used for replacing the previous relationship used Sensing and Modeling (FRAM) Field Project in the forecasting models. And Preliminary Results. AMS Cloud Physics Conference, Wisconsin, USA. AMS Cloud • Although a fit is not given for vis versus Physics Conf., July 10-15 2006, Madison, PR, the results are found to be comparable with Wisconsin, USA. earlier works and the theoretical calculations of Rasmussen et al (1999). Overall, a large Gultepe, I., M. D. Müller, and Z. Boybeyi, 2006: variability in the relationships suggests that vis- A New Visibility Parameterization for Warm PR relationships need to be improved. Fog Applications in Numerical Weather Prediction Models. J. Appl. Meteor., in Press. • The relationships between vis versus Nd Isaac, G.A., J.K. Ayers, M. Bailey, L. and Ni clearly indicated that number Bissonnette, B.C. Bernstein, S.G. Cober, N. concentration of the particles needs to be Driedger, W.F.J. Evans, F.
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