Global modeling of weather and applications for health research
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1 Global modeling of weather and applications for Dr. Karl G. Gutbrod TPH Basel
2 meteoblue AG, Basel a private Weather Information Management company based in Basel, Switzerland founded in 2006 high quality, efficiency and automation corporate clients in 21 countries by 2012, incl. Fortune 500 cooperation partners in weather, wind, energy management
3 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A
4 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A Coverage 3-D ground & air Lower Cost Reconstruct history
5 Significance of weather modeling Measuring or simulation? Weather modeling instead of measuring Better coverage 3-D: Ground and Air, Land and sea Lower cost Reconstruct history
6 Measurements: weather stations Observation Coverage ORACLE Synoptic land stations and ships Manual (red), automatic (green), land and manual (blue), ship and automatic (cyan) Date/Time: : :00 Source: MeteoSchweiz High precision Coverage variable Few Data for sea mountains deserts Rainforest etc. Many gaps (>99%)
7 modeling: 3-D Data for ground and surface Data for land and sea No gaps, Just imprecisions Land and Sea, Ground and Air
8 Significance of weather modeling: Coverage Coverage 60% to 0.01%, 50 km grid = 60% (EU) - 8% (Africa) 12 km grid = 4% (EU) - 0.6% (Africa) 3 km grid = 0.3% (EU) % (Africa) Coverage depends on continent and resolution Reality = 96% 3 km = 99.7%! Atmosphere not included!
9 Costs of measurement and modeling Measurement cost (EU) mill. modeling cost mill. Weather stations (3000) Satellites (3-5) Radar (100) Transmission & Processing Model cluster Model maintenance Verification Others TOTAL ~200 ~2-20 modeling = times cheaper
10 Model: Reconstruct history without measurements Above critical wind speed = crane downtime Simulation can go back years Any place on Earth can be recalculated Calibration with 1-year measurement Simulation enables to reconstruct the past
11 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A Measurements Assimilation Model Calculation Post-Processing
12 How does a forecast model work? Data (e.g. from measurements) Assimilation Forecast-Model Dynamic (Wind, ) Numeric Parametrisations (Precipitation, ) Post-processing
13 Measurement: Data sources
14 Measurements: weather stations Observation Coverage ORACLE Synoptic land stations and ships Manual (red), automatic (green), land and manual (blue), ship and automatic (cyan) Date/Time: : :00 Source: MeteoSchweiz High precision Coverage variable Few Data for sea mountains deserts rainforest etc. Many gaps (>99%)
15 Assimilation of data: Worldwide is the first step
16 How does a forecast model work? Data (e.g. from measurements) Assimilation Forecast-Model Dynamics (Wind, ) Numerics Parametrisation (Precipitation, ) Post-processing
17 Dividing space into grid boxes Global models have no lateral boundaries Grid box defines the weather
18 Post-processing: Removing systematic forecast error Error sources: Representativeness of stations Model deficiencies
19 Assimilation: normalizing data GFS (Global Forecasting System) Station 1 Station 3 Station 2 Data from different sources Data from different time steps Several Data for one grid cell and No data for most grid cells Must fill all grid cells Must be fast (1-2 hours) Assimilation: the most demanding step
20 How does a forecast model work? Data (e.g. from measurements) Assimilation Forecast-Model Dynamics (Wind, ) Numerics Parametrisation (Precipitation, ) Post-processing
21 Gridding the World: Example Globe divided in cells 50 km squares = cells Cell has 55 levels of boxes km altitude = 35 million boxes Hourly time steps One day = 840 million data points Weather = 10 parameters One day = 8.4 billion values Forecast is calculated 1-2(-4) x /day Global reality in 50 km steps = 8.4 billion data points / day
22 GFS Resolution: ~ 40 km Temperature simulation , 21:00 UTC Spatial resolution: 40 km Only major differences No topography details (mountains, valleys, lakes)
23 NMM Resolution: ~ 12 km Temperature simulation , 21:00 UTC Spatial resolution: 12 km Some local differences Some topography details (larger mountains, valleys, lakes)
24 NMM Resolution: ~ 3 km Temperature simulation , 21:00 UTC Spatial resolution: 3 km Many local differences Many topography details (mountains, valleys, lakes)
25 Resolution defines what a model sees Grid cells are equally spaced Calculation efforts increases factor 10 with ½ cell radius Box Volume Box length 1/2 = Calculation 10x 2 80 km 4 km Resolution 1 hour (0.04 days) hours (400 days) Calculation time (1 run) General Detailed Results Calculation time limits resolution
26 How does a forecast model work? Data (e.g. from measurements) Assimilation Forecast-Model Dynamics (Wind, ) Numerics Parametrisation (Precipitation, ) Post-processing
27 Numerics: Solving the equations!---pressure gradient force components, behind h do j=jds,jde do i=ids+1,ide pdxp=(dsg2(l)*pdx(i,j)+pdsg1(l))*0.5 ppx=(fim(i,j)-fim(i-1,j)) & *(dwdt(i-1,j,l)+dwdt(i,j,l))*pdxp pcx(i,j,l)=(rtop(i-1,j,l)+rtop(i,j,l)) & *(apel(i,j)-apel(i-1,j))*pdxp pgx(i,j)=ppx+pcx(i,j,l) enddo enddo do j=jds+1,jde do i=ids,ide pdyp=(dsg2(l)*pdy(i,j)+pdsg1(l))*0.5 Equations are solved using numerical approximations Massive computation exercise
28 Parametrisation: What is the resulting effect of a process? Does the wind blow around the buildings really change the weather?
29 Parametrisation: What is parametrised? Diffusion Consideration of effects which are: Too small for the grid Toocomplex Snow Turbulence Latent heat flux Convection Sensible Heat flux Too expensive to compute Vegetation Precipitation
30 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
31 Richardson s Weather Factory Richardson, 1922: Weather forecast by Numerical Process - Proposal: Global grid: = columns For each column, a team calculates the weather persons for the calculation of the weather in realtime - but not faster!! Model slower than reality
32 Computing power and Simulation-Quality Resolution (km) Vertical Atmosphere layers Values for 50 km Simulation = Forecast on 500 hpa (~5 km asl) Status in 2011; Resolution of 12 (3) Km 2-4 updates daily 7 (3) day forecast Hourly intervals Skill (days) Year Computing power drives forecast Skill = Statistically better quality than chance MIPS = Mill. instructions pro second
33 Forecast validation: comparing with measurements We publish our forecast validation results, because: we are transparent we provide quality we are realistic we are competitive
34 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
35 Forecast validation: comparing with measurements Important Parameters to be checked: Temperature Precipitation Wind speed Radiation
36 Temperature Temperature forecast forecast Absolute Error hourly data( K) Typ RAW 12 km 3 km imeteo 12km imeteo 3km MOS 12km MOS 3km Km Description Global Forecast System meteoblue NMM-12 meteoblue NMM-3 NMM-12 + Station observ. NMM-3 + Station observ. NMM-12 + p int + MOS NMM-3 + p int + MOS Observation total: 9.6 million datapoints Source: Universität Basel GFS: Global forecasting system (3h) NMM: Numerical Mesoscale Model imeteo = observation filtered forecast MOS = Model Output Statistics (based on observation) Simulation quality (<2 C hourly error) 3 Km produces 85% reality 3 Km+ filter produces >95% reality 3 Km+MOS produces >99% reality Stationen: Class Simulation Usefullness Need of observation Easy Good (can t get better) (generally OK) Simulation as Simulation good as very close to Observ. Observ. Fair (just about...>2 C) Simulation close to observation Difficult (with a little help..) Simulation with station support Impossible (not reality...) Simulation is not close to observ. For a < 2 C hourly mean error: 85% of places can be simulated 15% of places need observation
37 1-year wind statistic by weather station Wind Temperature forecast forecast Absolute Error hourly data(m/s) Typ RAW 12 km 3 km OF 12km OF 3km MOS 12km MOS 3km Km Description OF Global Forecast System meteoblue NMM-12 meteoblue NMM-3 NMM-12 + Station observ. NMM-3 + Station observ. NMM-12 + p int + MOS NMM-3 + p int + MOS Observation total: 7.2 million datapoints Source: Universität Basel GFS: Global forecasting system NMM: Numerical Mesoscale Model OF = observation filtered forecast MOS = Model Output Statistics (based on observation) Simulation quality (<2m/s hourly error) 3 Km produces 65% reality 3 Km+filter produces >80% reality 3 Km+MOS produces 95% reality Simulation Usefullness Stationen: Need Class of observation Good (generally OK) Simulation very close to Observ. Fair (just about...>2m/s) Simulation close to observation Difficult (with a little help..) Impossible (not reality...) Simulation with station support Simulation is not close to observe. For a < 2m/s hourly mean error: 65% of places can be simulated 35% of places need observation 10% can not be
38 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
39 Resolution: 2000 m Topography with no changes within 2 Km Simulation sufficient for Temp, Wind, (radiation) Observation for precipitation Simulation can cover reality
40 Resolution: 50 m Topography with changes every 50 Meters Observation needed for Temperature, wind, radiation, precipitation Simulation /observation to find out where to place plant...
41 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
42 Limits of predictability: chaos Certain systems are less stable System reacts to very small changes Initial state measurements not well known Weather simulation for 1-8 days better than climate averages or extrapolation (statistically)
43 Limits of predictability: chaos Sample: Description of isobar evolution over 9 days (Europe) Massive differences possible after 7-9 days
44 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
45 Combined Predictability (Temp,Wind & Dewpoint) Derived multivariate predictability index Hourly absolute errors of the MOS simulations scaled linearly between 0-1 smallest error with an index value of 0 and biggest error with an index value of 1 Clear regional US is very diverse, patterns Tropics & coast generally good. Continental climates more difficult. Virtually no small scale patterns, e.g. Alps are not visible (as in RAW). Source: Global Patterns of Predictability (Müller & Gutbrod 2014)
46 Available Models: EU Overview GFS GFS ECMWF...SMA.... GFS MO GFS DWD.KNMI... GFS MF MS GFS RU GFS JMA GFS meteoblue Global models 3 majors sources, 10 others 3-hourly data over >10 years Local area models (LAM) many sources, limited surface Distribution many silos zetta-bytes (10 21 bytes) of data Simulations are everywhere, multiple coverage (3-10x)
47 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A Lots of data Data for entire Earth Weather History Graphics on demand
48 largest private producer of Weather forecast volume Comparison with Largest Center in EU National provider: CH Status Capacity doubled
49 meteoblue models: worldwide coverage, focus on land
50 meteoblue models: hourly history for past 30 years Weather history and forecast Same source for past and next Detail reproduction of events Can see eye of the storm (and many other things) Weather visible in every place on Earth for past 30 years
51 meteoblue models: many presentation formats Weather history and forecast Forecast History Days Months Years Frequencies No more white spots on Earth for past 30 years
52 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A History Parameters Resolution Time range Regions
53 Diseases & environment Examples of disease weather relationship Influenza: cold or fast changes in temperatures Pollen allergies temperatures and precipitation Malaria moisture and temperature Respiratory problems temperature, humidity, aerosols Many others? Some diseases are driven by environment
54 Diseases & environment Malaria host : mosquito (Anopheles ) Eggs laid on water surface Need still water: coupled to rainfall Hatching in 2-21 days depends on Temperatures egg Infection Adult stage in 7-21 days Total Incubation : 9-42 days Critical level ofprecipitation Infection time and place predictable
55 Health & environment Air pollution the classic (smog) Air circulation in cities Heating & cooling in buildings Urban heat island Poroject BUBBLE Source: Climate change will change the health environment
56 meteoblue in
57 Weather modeling & Health: Overview 1. Weather modeling: Why modeling? Process overview Precision of modeling Modeling Output 2. Applications to Health 3. Q & A
58 Thanks for your attention
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