Project II.2: Fog and Low Clouds: improvement of 1D models

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1 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 2 Project II.2: Fog and Low Clouds: improvement of 1D models Coordinator: Lars Bergeas (from Feb. 1998) Military Weather Service, Sweden (to Jan 1998: Peter Clark, University of Reading/Met. Office, UK) Participants: Esbjorn Olsson Cristina Madeira Alfred Quinet SMHI, Sweden Instituto de Meteorologia, Portugal Belgian Met Institute, Belgium Executive Summary Due to a major reduction of the number of participants during the course of the project, the goals that were set up in the beginning have not been completely achieved. However comprehensive and interesting work has been carried out in some countries. In France experiments were made before the start of COST 78 to use a 1D model to forecast the formation and development of fog with encouraging results. Unfortunately this work could not be completed due to shift of responsibilities for several of the participants in the project. In Sweden a 1D model that is run for many points in a horizontal grid has been developed. The model utilises the latest information from a mesoscale analysis (MESAN) for the initial profiles. Dynamical forcing is taken from a 3D model (HIRLAM). Also the UKMO Road Surface Temperature Model shows great potential for predicting low clouds and fog. That model incorporates local orography and terrain. In both cases the results are encouraging and indicates that it is useful to further develop and utilise the models. One important outcome of the project is new knowledge of what projects are going on in this field and an updated list of contact persons. There has also been an exchange of ideas on how to improve the tools for forecasting fog and low clouds. List of figures Figure 1. Output from the 1D Hirlam model.

2 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 3 List of acronyms and symbols 1D ABL AMT ECAM HIRLAM IM MOS MESAN NOPEX NWP RiPP SMHI SSFM UKMO VSRF One dimensional Atmospheric boundary layer ( = PBL) Air mass transformation model European conference on applications of meteorology High resolution limited area model Instituto de Meteorologia (Portugal) Model output statistics Mesoscale analysis Northern hemisphere climate processes land surface experiment Numerical weather prediction Rationalisation in the production process Swedish Meteorological and Hydrological Institute Site specific forecast model United Kingdom Met. Office Very short range forecast

3 4 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 1 Introduction 1.1 Problem It is currently not possible to run 3D numerical models with the time and space resolution needed for local forecasts. Instead 1D models are used to refine the output from 3D models with the aim of improving vertical resolution and surface exchange processes but of necessity not taking full account of three dimensional processes. At the COST 78 first international workshop in Bologna March 1996 it was stated that problems could be addressed by a combination of high resolution 3D models and 1D models complemented by theoretical studies. 1.2 State of the Art 1D models are of two broad types: stand alone and coupled to 3D models. Both types can be applied either in situ or following air trajectories (Air Mass Transformation models). All rely on a description of the boundary layer processes. Coupling of 1D models to 3D models has shown the capability to increase the skill of forecast, particularly for variables such as near surface temperatures. For more difficult variables like low clouds only marginal improvements have been shown so far. It is believed that more progress can be made by further development. E. g. in Sweden development is being made on 1D model run for many points in a horizontal grid and coupled to dynamical input from HIRLAM. Also the UKMO Site Specific Forecast Model shows great potential for predicting both surface temperature and fog. 1.3 Objective Two objectives have been stated. 1. To promote the exchange of information on parametrization schemes in use in existing 1D models. In particular, experience regarding the deficiencies in schemes and on-going developments will be shared. 2. To investigate techniques to incorporate horizontal sub-grid scale forcing of 1D coupled models. 1.4 Scope The project includes 1D model formulations in connection with fog and low clouds, both stand alone and coupled. 1.5 Goal To improve the formulation of 1D models and exchange information. 2 Accomplished Activities A new rapporteur and co-ordinator was in Feb 1998 suddenly appointed for this project. Other remaining participants are only three by number. That implies a

4 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 5 reduction of five persons since the Bologna workshop. The project has consequently only focused on and resulted in exchange of ideas and results. However, it shall be acknowledged that some of the former participants have contributed to the final report. During combined meetings with other projects in COST 78 ideas have come up to use the output from 1D models as predictor for statistical methods and to use output from MOS, e. g. vertical profiles of temperature and humidity, as input to 1D models. However, the time available has not allowed any specific work on this idea. 2.1 Activities in Sweden SMHI (Esbjorn Olsson) Within the RiPP (Rationalisation in the Production Process) project at SMHI the 1D version of the HIRLAM model has been further developed. The model has been used operationally for some years to produce short range forecasts for all airports. See an example in figure 1. Figure 1. Output from the 1D Hirlam model. This example is a time cross section from the airport at Kiruna in northern Sweden. All parameters that are relevant for aviation are displayed. The red vertical lines denotes risk for icing. SMHI has recently managed to utilise the mesoscale analysis MESAN as an input, to run the 1D model for 22 km spaced gridpoints over Sweden, to produce nowcasts and VSRF of different parameters, such as temperature, cloudiness,

5 6 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models cloud base and visibility. This work was reported at ECAM 97 in Lindau. Test campaigns for evaluation has started in co-operation between SMHI and the Military Weather Service. Due to technical and programming problems there are no clear results available yet. The system for running a one-dimensional version of the Hirlam model on a grid has been thoroughly revised during the last year. During the first operational year it was noted that the model had some problems with the cloud forecasts. There was a negative bias in total cloud amount for the shortest forecast lengths and positive bias for the longer forecasts. The reason for that was not clear and therefore all components of the system have been checked in different ways. Experiments have been run with the full three-dimensional model in order to compare the dynamical tendencies produced for the 1D model to those in the 3D model. Some deficiencies have been found and corrected regarding the Semi- Lagrangian advection procedure. A lot of time has also been spent on the method that produces the initial profiles for the model. It is mainly the humidity profiles that are changed due to information from a mesoscale analysis on cloud amount, cloud base, cloud top and precipitation. There is no information on how many layers there are between the base and the top. Humidity is corrected above and below the analysed cloud top and cloud base respectively. Precipitation in the analysis means a minimum thickness of the initial cloud deck in the model. There are a lot of ways that this correction of the humidity profile can be done. The profile must be constructed in a way that the model accept so that the mesoscale information is retained in the model run. It is not clear how this can be achieved, so a lot of different approaches has been tested and as of today there is no final solution. Some verifications of the revised model have been done. The forecasts of total cloud cover from the 1D model is slightly better than those from the 3D model. The negative bias for the shortest forecasts seems to be gone, now there is a small positive bias for all forecasts lengths. No verification has been done on cloud base forecasts yet. SMHI has started a new project named Nowcasting and the Military Weather Service is participating. The goal for the project is among other things to improve very short forecasts for low clouds and precipitation based on MESAN and the 1D grid model mentioned above. One interesting approach which is going to be investigated is to nudge the moisture field towards the MESAN information and run a 3D model on a short time centred at the time of observation. Evaluation is going on at present. Uppsala University (Michael Tjernstroem) At the Bologna workshop it was told that Uppsala University planned to emphasise the combination of numerical modelling and analysis of field experiment data. The plan was to run 1D and 3D models for the NOPEX (Northern Hemisphere Climate Processes Land Surface Experiment) dataset

6 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 7 using the higher-order-closure mesoscale model developed at the University. Such runs has now partly taken place, in collaboration with a PhD student, Patrick Samuelsson at Uppsala University. One obvious drawback of using 1D models for stratocumulus forecasting is the simple fact that they assume horizontal homogeneity on scales that are smaller than that of the driving 3D model. A way to possibly remedy that is to use detailed high-resolution 3D simulations for cases where such a heterogeneity occurs, and attempt to parametrize the impact of these. Using field measurements from the NOPEX experiment, we have set up a series of high-resolution mesoscale simulations to asses the impact of a smaller (35 square km) lake in a patchy landscape dominated by coniferous forest. These simulations were interpreted partly by the aid of airborne meteorological measurements. The model results indicate three main patterns developing as a consequence of the different roughness and thermal inertia of the lake surface: 1/ During nocturnal conditions there was always a dipole-pattern of upwind divergence and downwind convergence over the lake, where the magnitude of the convergence was larger than that of the divergence. Aloft, this results in a vertical perturbation to the flow that decelerates the wind speed over the lake; 2/ During daytime conditions with moderate to strong winds, this pattern remains, although considerably weakened; 3/ For conditions with weaker winds (geostrophic winds lower than ~ 5-6 m/s), a lake-breeze will form, if the lake is large enough (> 2 times the depth of the PBL). The corresponding convergence zone appears at the upwind shoreline and bends over the side of the lake in a horse-shoe-like pattern. In this case there is a considerable divergence over the lake itself. The mesoscale contribution to the vertical fluxes of momentum, heat and water where entirely negligible for all the runs performed here. 2.2 Activities in UK (Peter Clark) The development stage of the UKMO Site Specific Forecast Model (SSFM) has been completed. This model is a 1D model, driven directly from output from operational NWP system, modified (in a simple way) to take some account of local orography. It is based on the UKMO Unified Model physics, run with much higher resolution in the sub-surface and boundary layers. It also has a greatly improved treatment of surface exchange, taking account of local heterogeneity (e.g. urban areas compared with open fields, forests or open water) by means of a 'tile' surface exchange scheme, with different surface contributions determined using a source area model applied to the upwind fetch. Currently the model runs as a pure post processing system for NWP products, and no use is made of local observations. The model has primarily been tested (and accepted) as a tool for predicting surface temperature and state for road transport, and has been shown to perform as well as human forecasters in this application. However, the model also improves on NWP output forecasting poor visibility and radiation fog, because of its improved vertical resolution and surface physics, and also due to its orographic modifications (which are most

7 8 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models effective at elevated sites). Trials comparing forecasts with visibility derived from operational TAFs show encouraging performance. Though not as skilful as forecaster-generated operational TAFs, they show less bias and a greater overall probability of detection of fog. Some improvement in performance is expected by making use of local observations and automatic nowcasts in modifying initial and forcing data. This work is now in progress. Though capable of realistic, and even accurate, simulations of radiation fog, the system shows some systematic errors. Apart from obvious (and largely inescapable) deficiencies in the treatment of local orography, one major problem appears to be the parametrization of turbulent exchange in very stable boundary layers; the scheme employed (along with others tested) appears to systematically over-predict near-surface wind-shear and turbulent fluxes, thereby (somewhat) inhibiting fog formation. The model has now been accepted for operational trials, and has been re-written to run for a large number of sites (i.e. several hundred) using the UKMO T3E super-computer. The model is controlled by a system running on dedicated workstations. This system also manages an ORACLE database, which stores forcing data and output from the SSFM, as well as delivering it to client systems. This database, and the system it forms a part of, is called the Forecasting for Specific Sites System Implementation (FSSSI), and also controls and runs the operational Kalman Filter Model Output Statistics system (KF/MOS). The FSSSI is also running a trial of an automated TAF generation program. The FSSSI will form the primary (and, eventually, only) source of automated point or 'sitespecific' forecasts from UKMO, eventually producing a combined forecast using a combination of SSFM and KF/MOS techniques. 2.3 Activities in other countries Portugal (Cristina Madeira) The Air Mass Transformation model (AMT) used in IM is a high-resolution, onedimensional atmospheric boundary-layer (ABL) model that is designed to move in a Lagrangian way along a low-level trajectory (Holtslag and De Bruijn, 1990, M. W. R., 118, ). As this ABL model moves along the predetermined trajectory, it undergoes spatial changes in its surface forcing. In addition to computing the changes implied by the variable surface fluxes, as the ABL column moves along, account must be taken also of the impact of differential advection in and above the ABL. This is accomplished by using a family of trajectories computed from the large scale model at different pressure levels. The initial profiles are built using the temperature and humidity analysis from a NWP model levels. The AMT first model level is set at 10m, the resolution is: 40m up to 300m, 50m up to 1000m, 100m up to 2000m and finally 200m up to 4000m. The ABL model prognostic equations used represent a first order non-local diffusion approch for potential temperature and specific humidity derived from K-theory, with inclusion of contragradient terms ( Troen and Marhrt, 1986, B.L.M., 37, ). To integrate these two equations and in order to ensure numerical stability, a fully implicit finite elements scheme is used. The surface fluxes over sea and over land are computed, respectively, by the profile and energy balance methods (Van Ulden

8 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 9 and Holtslag, 1985, J.C.A.M., 29, ). This model was used in a pre-operational mode for a year in the aeronautical division; twice a day (for 00 and 12UTC runs) 24 h forecasts were made for 3 aeroports over Portugal mainland. The outputs provided to the forecasters were: trajectory charts, temperature and humidity profiles, 2 metre temperature and 2 metre dew point, stability, boundary layer height and low level clouds (amount and height). The objective verification was not very encouraging concerning its usage in operational forecasting, despite some case studies showed a good performance of the model in particular situations. Work is planned to use this AMT 1D model coupled with a mesoscale model in order to mitigate some of the problems encountered with its operational usage with the global model. Belgium (Alfred Quinet) A stand alone type of 1D model is used which takes into account IR radiative transfer, boundary layer dynamics and heat fluxes. Initial conditions are given by radio-sounding profiles of temperature and humidity. Still to be done: Introduce solar radiation transfers and criteria to be met for fog appearance.

9 10 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 3 Summary of results All original expectations on the project have unfortunately not been met. This is partly due to the fact that the project has suffered from a reduction in manpower by more than half. The project has consequently only focused on and resulted in exchange of ideas and results. In France experiments have been carried out, before the start of COST 78, to forecast fog with 1D models. The results are interesting but showed to be very sensitive to local conditions. In Sweden development and evaluation of a 1D model run in a horizontal grid has taken place. The model gets its initial data from a 3D NWP model (HIRLAM) first guess field and latest observations or mesoscale analysis (MESAN). The results have been rather good although the model has suffered from some program errors. In UK a newly developed site specific forecast model to predict road surface temperature has shown a capability to forecast fog as well. In Portugal evaluations are being made on a 1D air mass transformation (AMT) model connected with a large scale NWP model. In this 1D model the impact of differential advection is taken into account. 4 Recommendations It is probably useful to utilise and combine several of the experiences and ideas on 1D models that have been revealed in this project. A combination of the utilisation of local terrain features in UK and the use of local observations, or mesoscale analysis, in the Swedish approach would for instance be very interesting. Experiments utilising the output from 1D models as predictor to model output statistics would probably also be of use to the application of 1D models. The members in the project strongly believes that further development of 1D models would be of great importance to improve the methods for nowcasting. It is important to have an updated list of contact persons and try to increase the exchange of information. We believe that the overall goal for COST 78 of improvement and standardisation of methods eventually will be achieved to some extent that way. 5 References

10 COST 78- Project II.2 Fog and Low Clouds: Improvement of 1D models 11 Bergot, T and Guedalia, D, 1994: Numerical forecasting of radiation fog. Part I: Numerical model and sensitivity tests. Mon. Wea. Rev., 122, Clark, P. A, 1997: Development of an automated site-specific forecast model. Annalen der Meteorologie 35 (Proceedings of ECAM 97), Gollvik, S. And Olsson, E, 1995: A one-dimensional interpretation for detailed short-range forecasting. Met. Applications, vol 2, Guedalia, D. and Bergot, T. 1994: Numerical forecasting of radiation fog. Part II: A comparison of model simulation with several observed fog events. Mon. Wea. Rev., 122, Olsson, E. 1997: HIRLAM 1D; A one-dimensional version of the HIRLAM model. Annalen der Meteorologie 35 (Proceedings of ECAM 97), Authors Lars Bergeas, Swedish Armed Forces HQ, Weather Service Department, S Stockholm, Sweden lars.bergeas@hkv.mil.se Peter Clark, The Joint Centre for Mesoscale Meteorology University of Reading/Meteorological Office, UK paclark@meto.gov.uk Cristina Madeira, Instituto de Meteorologia, Rua C do Aeroporto, 1700 Lisboa, Portugal cristina.madeira@meteo.pt Esbjorn Olsson, SMHI, S Soerberge, Sweden eolsson@smhi.se Michael Tjernstroem, Department of Meteorology, Stockholm University, Arrheniuslaboratory, S Stockholm, Sweden michaelt@misu.su.se Alfred Quinet, Belgian Met. Institute, 3 Av Circulaire, B 1180 Bruxelles, Belgium quinet@oma.be