Modern and prospective technologies for weather modification activities: A look at integrating unmanned aircraft systems

Abstract Present-day weather modification technologies are scientifically based and have made controlled technological advances since the late 1990s, early 2000s. The technological advances directly related to weather modification have primarily been in the decision support and evaluation based software and modeling areas. However, there have been some technological advances in other fields that might now be advanced enough to start considering their usefulness for improving weather modification operational efficiency and evaluation accuracy. We consider the programmatic aspects underlying the development of new technologies for use in weather modification activities, identifying their potential benefits and limitations. We provide context and initial guidance for operators that might integrate unmanned aircraft systems technology in future weather modification operations.

[1]  Don A. Griffith,et al.  The Scientific Basis , 2006 .

[2]  S. Lance,et al.  Coincidence Errors in a Cloud Droplet Probe (CDP) and a Cloud and Aerosol Spectrometer (CAS), and the Improved Performance of a Modified CDP , 2012 .

[3]  Mark C. L. Patterson,et al.  Atmospheric and ocean boundary layer profiling with unmanned air platforms , 2014, 2014 Oceans - St. John's.

[4]  M. V. Ramana,et al.  Albedo, atmospheric solar absorption and heating rate measurements with stacked UAVs , 2007 .

[5]  A. Sterkin,et al.  Sensitivity of raindrop formation in ascending cloud parcels to cloud condensation nuclei and thermodynamic conditions , 2004 .

[6]  Pradip M. Jawandhiya,et al.  Review of Unmanned Aircraft System (UAS) , 2013 .

[7]  Shannon T. Brown,et al.  NASA's Genesis and Rapid Intensification Processes (GRIP) Field Experiment , 2012 .

[8]  William L. Woodley,et al.  Simulation of hurricane response to suppression of warm rain by sub-micron aerosols , 2007 .

[9]  Alan Hobbs,et al.  Unmanned Aircraft Systems , 2010 .

[10]  Manuel Vega,et al.  High-Altitude Imaging Wind and Rain Airborne Radar (HIWRAP) , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Eric W. Frew,et al.  The tempest unmanned aircraft system for in situ observations of tornadic supercells: Design and VORTEX2 flight results , 2011, J. Field Robotics.

[12]  V. S. Scott,et al.  Cloud physics lidar: instrument description and initial measurement results. , 2013, Applied optics.

[13]  R. Rasmussen,et al.  Evaluating Winter Orographic Cloud Seeding: Design of the Wyoming Weather Modification Pilot Project (WWMPP) , 2014 .

[14]  R. Rasmussen,et al.  The impact of ground-based glaciogenic seeding on clouds and precipitation over mountains: A multi-sensor case study of shallow precipitating orographic cumuli , 2014 .

[15]  J. Golden,et al.  Extra area effects of cloud seeding — An updated assessment☆ , 2014 .

[16]  Martin Perrine,et al.  The NASA High-Altitude Imaging Wind and Rain Airborne Profiler , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Joshua P. Schwarz,et al.  A light-weight, high-sensitivity particle spectrometer for PM2.5 aerosol measurements , 2016 .

[18]  Ross N Hoffman Controlling hurricanes. , 2004, Scientific American.

[19]  Thomas P. DeFelice,et al.  An introduction to meteorological instrumentation and measurement , 1998 .

[20]  Roelof T. Bruintjes,et al.  A Review of Cloud Seeding Experiments to Enhance Precipitation and Some New Prospects , 1999 .

[21]  S. Chai,et al.  The Assessment of Snowpack Enhancement by Silver Iodide Cloud-Seeding using the Physics and Chemistry of the Snowfall , 1996 .

[22]  A miniature scanning sun photometer for vertical profiles and mobile platforms , 2016 .

[23]  M. Murakami,et al.  Effect of hygroscopic seeding on warm rain clouds – numerical study using a hybrid cloud microphysical model , 2009 .

[24]  Matthew G. Kowalewski,et al.  Remote sensing capabilities of the Airborne Compact Atmospheric Mapper , 2009, Optical Engineering + Applications.

[25]  Linda M. Northrop,et al.  CMMI Distilled : A Practical Introduction to Integrated Process Improvement , 2022 .

[26]  John Latham,et al.  Sea-going hardware for the cloud albedo method of reversing global warming , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  K. J. Allwine,et al.  OVERVIEW OF URBAN 2000 A Multiscale Field Study of Dispersion through an Urban Environment , 2002 .

[28]  M. Richardsonb,et al.  A light-weight , high-sensitivity particle spectrometer for PM 2 . 5 aerosol measurements , 2016 .

[29]  G. K. Mather,et al.  Calculations Pertaining to Hygroscopic Seeding with Flares , 1997 .

[30]  M. Gallagher,et al.  The backscatter cloud probe – a compact low-profile autonomous optical spectrometer , 2013 .

[31]  J. Minx,et al.  Climate Change 2014 : Synthesis Report , 2014 .

[32]  R. Rasmussen,et al.  Implementation of a silver iodide cloud-seeding parameterization in WRF. Part II: 3D simulations of actual seeding events and sensitivity tests , 2013 .

[33]  Andrea I. Flossmann,et al.  A Numerical Study on the Impact of Hygroscopic Seeding on the Development of Cloud Particle Spectra , 2002 .

[34]  Z. Levin,et al.  On the Response of Radar-Derived Properties to Hygroscopic Flare Seeding , 2001 .

[35]  Paul Newman,et al.  Hurricane and Severe Storm Sentinel (HS3) , 2010 .

[36]  Beat Schmid,et al.  Solar spectral radiative forcing during the Southern African Regional Science Initiative , 2003 .

[37]  Todd Gaier,et al.  The High-Altitude MMIC Sounding Radiometer for the Global Hawk Unmanned Aerial Vehicle: Instrument Description and Performance , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[38]  E. Loth,et al.  Ice Adhesion Performance of Superhydrophobic Coatings in Aerospace Icing Conditions , 2015 .

[39]  P. DeMott,et al.  An Application of Chemical Kinetic Theory and Methodology to Characterize the Ice Nucleating Properties of Aerosols Used for Weather Modification , 1983 .

[40]  Steven D. Miller,et al.  The Department of Energy's Atmospheric Radiation Measurement (ARM) Unmanned Aerospace Vehicle (UAV) Program , 2000 .

[41]  A. Mangold,et al.  Experimental investigation of homogeneous freezing of sulphuric acid particles in the aerosol chamber AIDA , 2002 .

[42]  M. McNutt The drought you can't see , 2014, Science.

[43]  N. Savich Comparative analysis of Venusian ionosphere dual-frequency radio soundings with the satellites Venera-9, 10 and Pioneer-Venus , 1981 .

[44]  V. Chandrasekar,et al.  A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation , 2014 .

[45]  R. Rasmussen,et al.  The Dispersion of Silver Iodide Particles from Ground-Based Generators over Complex Terrain. Part II: WRF Large-Eddy Simulations versus Observations , 2014 .

[46]  Z. Levin,et al.  Seeding Convective Clouds with Hygroscopic Flares: Numerical Simulations Using a Cloud Model with Detailed Microphysics , 2000 .

[47]  T P DeFelice A High-level Atmospheric Management Program Plan for the New Millennium , 2002 .

[48]  Albin J. Gasiewski,et al.  Altair unmanned aircraft system achieves demonstration goals , 2006 .

[49]  David E. Jordan,et al.  THE NASA AIRBORNE TROPICAL TROPOPAUSE EXPERIMENT: High-Altitude Aircraft Measurements in the Tropical Western Pacific. , 2017, Bulletin of the American Meteorological Society.

[50]  J. L. Sánchez,et al.  Hail prevention by ground-based silver iodide generators: Results of historical and modern field projects , 2016 .

[51]  Michael Behar Can We Stop Storms , 2005 .

[52]  R. Nicoll,et al.  The brain's own marijuana. , 2004, Scientific American.

[53]  Richard J. Blakeslee,et al.  Lightning Imaging Sensor (LIS) for the International Space Station , 2001 .

[54]  P. Quinn,et al.  Measurements of Atmospheric Aerosol Vertical Distributions above Svalbard, Norway using Unmanned Aerial Systems (UAS) , 2013 .

[55]  V. Chandrasekar,et al.  Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification , 2000 .

[56]  Kang Sun,et al.  Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles , 2012, Remote. Sens..

[57]  S. Agashe,et al.  Feasibility study of artificial rainfall system using ion seeding with high voltage source , 2014 .

[58]  Maria Fabrizia Buongiorno,et al.  Unmanned Aerial Mass Spectrometer Systems for In-Situ Volcanic Plume Analysis , 2015, Journal of The American Society for Mass Spectrometry.

[59]  P. Kucera,et al.  Polarimetric Cloud Analysis and Seeding Test (POLCAST) , 2008 .

[60]  M. Dixon,et al.  TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology , 1993 .

[61]  Brad Baker,et al.  An overview of microphysical properties of Arctic clouds observed in May and July 1998 during FIRE ACE , 2001 .

[62]  Po-Hsiung Lin,et al.  OBSERVATIONS: The First Successful Typhoon Eyewall-Penetration Reconnaissance Flight Mission Conducted by the Unmanned Aerial Vehicle, Aerosonde , 2006 .

[63]  D. Rosenfeld,et al.  A Quest for Effective Hygroscopic Cloud Seeding , 2010 .

[64]  Kimon P. Valavanis,et al.  On unmanned aircraft systems issues, challenges and operational restrictions preventing integration into the National Airspace System , 2008 .