An Introduction of Unmanned Aerial Systems (UAS) for Weather Operations

Transcription

1 An Introduction of Unmanned Aerial Systems (UAS) for Weather Operations Chotipong Chamchalaem Graduate Seminar Spring 2017 Cal Poly San Luis Obispo

2 Motivation Interest in exploring new applications for UAS UAS is growing for many applications Birds Eye View: aerial photography, mapping Delivery: delivering packages Improve weather observation and forecasting Observation/Measurements Weather Model Cal Poly San Luis Obispo

3 Agenda Introduction to Weather Operation Initial Work Methodology Realization Change in Direction On-Going Work Expected Outcome Cal Poly San Luis Obispo

4 Weather Operation National Oceanic and Atmospheric Administration National Weather Service Climate Research Oceans & Coasts Fisheries Satellites Research Marine & Aviation Charting Sanctuaries Weather Underground WeatherSpark AccuWeather The Weather Company

5 What is Weather Forecasting? Numerical Weather Prediction Weather Model Mathematical, dynamic equations to predict the weather Initial conditions Provided by observation platforms Measurements of Temperature, Wind, Pressure, etc. INITIAL CONDITIONS Examples: Temperature, Wind speed/direction, moisture, Surface pressure WEATHER MODEL + = FORECAST (Mathematics, Thermal, and Fluid Analysis)

6 Weather Model A set of PDE s and equations that describe the dynamic and thermodynamic processes in the Earth s atmosphere Conservation of momentum Conservation of mass Conservation of energy Density, Pressure, and Temperature equation.

7 Observation Platforms Observation Remote Sensing Weather Model In-Situ Measurement Cal Poly San Luis Obispo

8 Remote Sensing and In-Situ Measurements In-Situ Measurements: taking local measurements Temperature, humidity, pressure, wind Calibrate/verify satellite observation data Ex/ thermometer, barometer, anemometer Remote Sensing: obtaining information from a distance Uses radiometers to scan Earth to form images Detects visible, infrared, and microwave radiation Derives temperature, moisture, precipitation Ex/ Radar Cal Poly San Luis Obispo

9 Initial Work Compare UAS and existing platforms (Satellites) using a common metric to identify requirements But found that requirements are different (Space vs Air) Then I decided to derive the most important aspects to platforms in terms of observation. Temporal Resolution and Spatial Resolution So I came up with this analysis between SAT and UAS for Temporal and Spatial Resolution

10 LEO SAT vs UAS Compare UAS with a weather observation platform (LEO SAT) Simulate UAS and SAT metrics using System Tool Kit Metric: Coverage Spatial Resolution Temporal Resolution Potential Benefits Networked Flexibility Maintainability Cost beneficial Metric Current Weather Satellite Weather Observation compare Potential UAS Metric Cal Poly San Luis Obispo

11 Methodology Calculate the swath distance of the instrument Use swath distance calculated to simulate UAS coverage Simulate LEO SAT and UAS using System Tool Kit Projected over Earth Customized orbits and flight path Visualize coverage Compare the distance UAS travels to satellite Compare the coverage Cal Poly San Luis Obispo

12 Coverage Set Up Equations d flat = h tan α d flat β = sin 1 R E d surf = R E 2β Assumptions Same swath angle as satellite Perfect spherical Earth Values α = 55.4 o h = 30 km d flat = km β = o d surf = km Swatch Distance= 2 x d surf = 87 km Cal Poly San Luis Obispo

13 Simulation Set Up Satellite Inputs: Altitude = 600 km Inclination = 98 o Swath distance = 2,000 km UAS Inputs: Altitude = 30 km Ground speed = 575 km/h Swath distance = 87 km Flight path set on equator Simultaneously run satellite and UAS simulation Cal Poly San Luis Obispo

14 Initial Simulation - Location of LEO SAT - Location of UAS Initial scenario for one cycle of polar orbit revisit in Systems Tool Kit Cal Poly San Luis Obispo

15 End Simulation ~ 12 hours later Final scenario for one cycle of polar orbit revisit in Systems Tool Kit - Location of LEO SAT - Location of UAS from previous - Location of UAS after 12 hour simulation - Distance traveled (~7,000 km) Cal Poly San Luis Obispo

16 Coverage Comparison Changed to flight path to follow satellite orbit path ~23 UAS to match 1 satellite coverage Keep in mind UAS is traveling slower Swath coverage of 8 30km and LEO 600km Cal Poly San Luis Obispo

17 Methodology (MATLAB) Use MATLAB to analyze graphical representation Simplification Assumptions: Earth treated as a single strip of land Width = Swath Distance (2 x d surf ) Length = Earth s Surface Area / surface coverage distance

18 Methodology (MATLAB) Simplification Assumptions: UAS flight path is a straight line # of UAS is dependent on the Revisit Time and UAS ground speed Distance Covered = UAS GS x Revisit Time # of UAS = Earth s Surface Area/2d surf Distance Covered/hr UAS GS

19 Sample Calculation UAS GS = 575 km hr Revisit Time = 12 hr Distance Covered = UAS GS x Revisit Time = 6900 km # of UAS = Earth s Surface Area Distance Covered = 850

20 Result

21 Conclusion With UAS attempting to provide similar coverage to polar orbiting satellites (global coverage) Will need an unreasonable fleet UAS altitude limits coverage significantly Realization: Weather operation has many systems of systems This study should not be focused on about how UAS can replace/do better at a specific platform But to find a fitting area UAS can assist weather operations Cal Poly San Luis Obispo

22 Weather Platform Timeline (1800s) Weather stations and kites introduced 1849: Smithsonian Institute supplied weather instruments to telegraph 500 stations across the U.S. by 1860s 1869: The ability to observe and simultaneously display observed weather data began advancement of weather forecasting Needed structure and organization 1894: Kites were used to self record temperatures aloft

23 Weather Platform Timeline (1900s) Weather aircrafts, balloons, radars, ships, and satellites introduced 1904: The government began using airplanes to conduct upper air atmospheric research 1909: Weather Balloon program began 1940: Atlantic Weather Observation Service 1954: 1 st Gen Weather Radar 1960: First Weather Polar Orbiting satellite 1973: 2 nd Gen Weather Radar 1975: First GEO satellite 1976: 3 rd Gen Weather Radar

24 Weather Platform Timeline (1900s) continued $4.5 billion overhaul of structure and operations Late 1900s: Automated Surface Observing System Next Generation Weather Radars New series of satellites Advanced computational systems Advanced Weather Interactive Processing System

25 Weather Platform Timeline (2000s) Increase in computation Satellite additions 2007: IBM supercomputers 14 trillion calculations per second 240+ million initial conditions

26 Summary Weather Forecast: Observation, collecting data side Data analysis and computation Supercomputers First came the weather stations and weather balloons Hand calculations limited their delivery time More calculators means faster delivery time The limit was on the # of observations LEO SAT, provides higher resolution and global coverage Remote sensing relies on in-situ measurements for calibration Only 1 observation per 12 hours GEO SAT, provides constant coverage over an area Also relies on in-situ measurements as a calibration tool

27 New Found Approach Objective: determine a weakness in the system and introduce UAS solutions Method: GAP Analysis Used to find gaps and holes inside current operations/methods Analyzes the current state and determines what is needed to reach the desired state In this case, analyze the current state of weather forecasting using GAP Analysis and determine how UAS can assist to improve forecasts

28 GAP Analysis Process Goals Current Achievements Gaps Causes Evaluate Knowledge Motivation Organizational Barriers Implementation Solutions Root Causes

29 GAP Analysis Process Goals What is our performance goal? Starts with defining the problem Problem then helps define the goal Who is going to do what? By when? How are we going to measure it?

30 GAP Analysis Process Goals Current Achievements Where are we now related to the goal? Observations Surveys Documents

31 GAP Analysis Process Goals Current Achievements Gaps What is the size of the gap between our current status and the goal we ve established?

32 GAP Analysis Process Goals Current Achievements Gaps Causes What is causing the gap?

33 GAP Analysis Process Gaps Causes Knowledge Knowledge Can they do it, do they know how to do it?

34 GAP Analysis Process Gaps Causes Knowledge Motivation Motivation Hard to see, internal. Do they value it? Have they started? Persistence?

35 GAP Analysis Process Gaps Causes Knowledge Motivation Organizational Barriers Organization Is there a process, procedure that inhibits stakeholders to achieve goal? Stakeholders have a shared value/belief?

36 GAP Analysis Process Gaps Causes Knowledge Motivation Organizational Barriers Root Causes

37 GAP Analysis Process Gaps Causes Knowledge Motivation Organizational Barriers What solutions will close the gap? Solutions Root Causes

38 Solutions Knowledge gaps require: Information Job aids Training Education Motivation gaps require: Self efficacy Values Mood Ex/ Confidence, Value of the goal, Concrete goals Organization gaps require: Process & policies changes Changes in culture Ex/ Transparency, Communication, Resources

39 Gap Analysis Process Gaps Causes How do we implement the solutions as an integrated package? Knowledge Motivation Organizational Barriers Implementation Solutions Root Causes

40 Gap Analysis Process Goals How do we measure our progress towards achieve out goals? Causes Evaluate Knowledge Motivation Organizational Barriers Implementation Solutions Root Causes

41 Ongoing Work Complete GAP analysis on Weather Operations Upon findings, analyze how UAS application can assist Perform spatial and temporal resolution comparisons between UAS and current Weather Aircraft

42 Expected Outcome Provide a detailed study on the implementation of UAS technology in the civilian world Provide an initial study on challenging current weather operations to provide solutions for improvement Provide an understanding of the heavily integrated weather operation and how UAS can assist

43 Thank you for your time! Cal Poly San Luis Obispo