PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

Robert Jackson; Scott Collis; Timothy Lang; Corey Potvin; Todd Munson

doi:10.5334/jors.264

Abstract

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.

Funding statement: The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which is funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.

References

Kosiba, K, Wurman, J, Richardson, Y, Markowski, P, Robinson, P and Marquis, J 2013 Genesis of the Goshen County, Wyoming, Tornado on 5 June 2009 during VORTEX2. Monthly Weather Review, 141: 1157–1181. DOI: 10.1175/MWR-D-12-00056.1
Open DOI Search in Google Scholar Back to article
Kosiba, K A and Wurman, J 2014 Finescale Dual-Doppler Analysis of Hurricane Boundary Layer Structures in Hurricane Frances (2004) at Landfall. Monthly Weather Review, 142: 1874–1891. DOI: 10.1175/MWR-D-13-00178.1
Open DOI Search in Google Scholar Back to article
Fast, J D, Newsom, R K, Allwine, K J, Xu, Q, Zhang, P, Copeland, J and Sun, J 2008 An Evaluation of Two NEXRAD Wind Retrieval Methodologies and Their Use in Atmospheric Dispersion Models. J. Appl. Meteor. Climatol, 47: 2351–2371. DOI: 10.1175/1520-0477(1993)074<;1669:TWATWO>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Crum, T D and Alberty, R L 1993 The WSR-88D and the WSR-88D Operational Support Facility. Bull. Amer. Meteor. Soc., 74: 1669–1688, DOI: 10.1175/1520-0477(1993)074¡;1669:TWATWO¿2.0.CO;2
Open DOI Search in Google Scholar Back to article
NOAA National Weather Service (NWS) Radar Operations Center 1991 NOAA Next Generation Radar (NEXRAD) Level 2 Base Data. [KLTX, KMHX reflectivity, Doppler velocity]. NOAA National Centers for Environmental Information. DOI: 10.7289/V5W9574V [30 Oct 2018].
Open DOI Search in Google Scholar Back to article
Gal-Chen, T 1978 A Method for the Initialization of the Anelastic Equations: Implications for Matching Models with Observations. Mon. Wea. Rev., 106: 587–606. DOI: 10.1175/1520-0493(1978)106<;0587:AMFTIO>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Gal-Chen, T and Kropfli, R A 1984 Buoyancy and Pressure Perturbations Derived from Dual-Doppler Radar Observations of the Planetary Boundary Layer: Applications for Matching Models with Observations. J. Atmos. Sci., 41: 3007–3020. DOI: 10.1175/1520-0469(1984)041<;3007:BAPPDF>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Lorenc, A C 1986 Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112: 1177–1194. DOI: 10.1002/qj.49711247414
Open DOI Search in Google Scholar Back to article
Shapiro, A, Potvin, C K and Gao, J 2009 Use of a vertical vorticity equation in variational dual-Doppler wind analysis. J. Atmos. Oceanic Technol., 26: 2089–2106. DOI: 10.1175/2009JTECHA1256.1
Open DOI Search in Google Scholar Back to article
Gao, J, Xue, M, Shapiro, A and Droegemeier, K K 1999 A Variational Method for the Analysis of Three-Dimensional Wind Fields from Two Doppler Radars. Mon. Wea. Rev., 127: 2128–2142. DOI: 10.1175/1520-0493(1999)127<;2128:AVMFTA>2.0.CO;2
Open DOI Search in Google Scholar Back to article
Potvin, C K, Shapiro, A and Xue, M 2012 Impact of a Vertical Vorticity Constraint in Variational Dual-Doppler Wind Analysis: Tests with Real and Simulated Supercell Data. J. Atmos. Oceanic Technol., 29: 32–49. DOI: 10.1175/JTECH-D-11-00019.1
Open DOI Search in Google Scholar Back to article
Potvin, C K, Betten, D, Wicker, L J, Elmore, K L and Biggerstaff, M I 2012 3DVAR versus Traditional Dual-Doppler Wind Retrievals of a Simulated Supercell Thunderstorm. Mon. Wea. Rev., 140: 3487–3494. DOI: 10.1175/MWR-D-12-00063.1
Open DOI Search in Google Scholar Back to article
Miller, L J and Fredrick, S M 1998 Custom Editing and Display of Reduced Information in Cartesian space (CEDRIC) manual. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division. Boulder, CO, 1–130.
Search in Google Scholar Back to article
Lang, T, Souto, M, Khobahi, S and Jackson, B 2017 nasa/MultiDop: MultiDop v0.3. Version v0.3. Zenodo. DOI: 10.5281/zenodo.1035904
Open DOI Search in Google Scholar Back to article
Oliphant, T E 2006 A guide to NumPy. USA: Trelgol Publishing.
Search in Google Scholar Back to article
Oliphant, T E, Peterson, P, et al. 2001 SciPy: Open Source Scientific Tools for Python. Available at: http://www.scipy.org/ [Online; accessed 2018-10-19].
Search in Google Scholar Back to article
Met Office 2015 Cartopy: a cartographic python library with a matplotlib interface. Exeter, Devon, available at: http://scitools.org.uk/cartopy.
Search in Google Scholar Back to article
Hunter, J D 2007 Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3): 90–95. DOI: 10.1109/MCSE.2007.55
Open DOI Search in Google Scholar Back to article
Helmus, J J and Collis, S M 2016 The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software, 4(1): e25. DOI: 10.5334/jors.119
Open DOI Search in Google Scholar Back to article
Liu, D C and Nocedal, J 1989 On the Limited Memory Method for Large Scale Optimization. Mathematical Programming B, 45(3): 503–528. DOI: 10.1007/BF01589116
Open DOI Search in Google Scholar Back to article
Collis, S, Protat, A, May, P T and Williams, C 2013 Statistics of Storm Updraft Velocities from TWP-ICE Including Verification with Profiling Measurements. J. Appl. Meteor. Climatol., 52: 1909–1922. DOI: 10.1175/JAMC-D-12-0230.1
Open DOI Search in Google Scholar Back to article
Blaylock, B K, Horel, J D and Liston, S T 2017 Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output. Computers & Geosciences, 109: 43–50. DOI: 10.1016/j.cageo.2017.08.005
Open DOI Search in Google Scholar Back to article
Horel, J and Blaylock, B 2018 Archive of the High Resolution Rapid Refresh Model. Dataset. University of Utah, DOI: 10.7278/S5JQ0Z5B.
Open DOI Search in Google Scholar Back to article
Roubicek, T, Calculus of variations 2014 Chap.17 In: Grinfield, M (ed.), Mathematical Tools for Physicists, 551–588. Weinheim: J. Wiley.
Search in Google Scholar Back to article

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

Abstract

Paradigm

My account