diff --git a/examples/plotting/plot_nexrad_image_muted_reflectivity.py b/examples/plotting/plot_nexrad_image_muted_reflectivity.py index 3ae91579a2..f11971ad5c 100644 --- a/examples/plotting/plot_nexrad_image_muted_reflectivity.py +++ b/examples/plotting/plot_nexrad_image_muted_reflectivity.py @@ -11,17 +11,14 @@ with just snow and just rain are depicted in a corresponding full-color scale. The ultimate utility of image muting radar reflectivity is to reduce the misinterpretation of regions of melting or mixed precipitation as opposed to heavy snow or heavy rain. +See Tomkins et al. (2022) for full details. """ print(__doc__) -# Author: Laura Tomkins (lmtomkin@ncsu.edu) +# Author: Laura Tomkins (lauramtomkins@gmail.com) # License: BSD 3 clause -# citation: Tomkins, L. M., Yuter, S. E., Miller, M. A., and Allen, L. R., 2022: -# Image muting of mixed precipitation to improve identification of regions -# of heavy snow in radar data. Atmos. Meas. Tech., 15, 5515–5525, -# https://doi.org/10.5194/amt-15-5515-2022 import matplotlib.colors as mcolors import matplotlib.pyplot as plt @@ -65,3 +62,10 @@ display.set_limits((-300, 300), (-300, 300)) ax.set_aspect("equal") plt.show() + +# References +# ---------- +# Tomkins, L. M., Yuter, S. E., Miller, M. A., and Allen, L. R., 2022: +# Image muting of mixed precipitation to improve identification of regions +# of heavy snow in radar data. Atmos. Meas. Tech., 15, 5515–5525, +# https://doi.org/10.5194/amt-15-5515-2022 diff --git a/examples/retrieve/plot_cfad.py b/examples/retrieve/plot_cfad.py index cffeba98ff..8e050748c5 100644 --- a/examples/retrieve/plot_cfad.py +++ b/examples/retrieve/plot_cfad.py @@ -7,7 +7,7 @@ print(__doc__) -# Author: Laura Tomkins (lmtomkin@ncsu.edu) +# Author: Laura Tomkins (lauramtomkins@gmail.com) # License: BSD 3 clause diff --git a/examples/retrieve/plot_feature_detection.py b/examples/retrieve/plot_feature_detection.py index 81371a3957..2654eab71e 100644 --- a/examples/retrieve/plot_feature_detection.py +++ b/examples/retrieve/plot_feature_detection.py @@ -9,7 +9,7 @@ print(__doc__) -# Author: Laura Tomkins (lmtomkin@ncsu.edu) +# Author: Laura Tomkins (lauramtomkins@gmail.com) # License: BSD 3 clause @@ -28,8 +28,8 @@ # varies with the background value. The algorithm is heavily customizable and is designed to work with a variety of # datasets. Here, we show several examples of how to use the algorithm with different types of radar data. # -# See Steiner et al. (1995), Yuter and Houze (1997), and Yuter et al. (2005) for full details on the algorithm. A -# manuscript (Tomkins et al. 2024) is in prep to describe feature detection in cool-season events (part 2). +# See Steiner et al. (1995), Yuter and Houze (1997), and Yuter et al. (2005) for full details on the algorithm. Tomkins +# et al. 2024 builds on this work to describe feature detection in cool-season events (part 2). ###################################### # Part 1: Warm-season convective-stratiform classification @@ -51,6 +51,7 @@ grid_shape=(1, 201, 201), grid_limits=((0, 10000), (-50000.0, 50000.0), (-50000.0, 50000.0)), fields=["reflectivity_horizontal"], + nb=1.5, ) # get dx dy @@ -189,6 +190,7 @@ grid_shape=(1, 201, 201), grid_limits=((0, 10000), (-200000.0, 200000.0), (-200000.0, 200000.0)), fields=["reflectivity"], + nb=1.5, ) # get dx dy @@ -421,6 +423,7 @@ grid_shape=(1, 201, 201), grid_limits=((0, 10000), (-200000.0, 200000.0), (-200000.0, 200000.0)), fields=["reflectivity", "cross_correlation_ratio"], + nb=1.5, ) # image mute grid object @@ -807,3 +810,8 @@ def quick_image_mute(field, muted_ref): # Image muting of mixed precipitation to improve identification of regions # of heavy snow in radar data. Atmos. Meas. Tech., 15, 5515–5525, # https://doi.org/10.5194/amt-15-5515-2022 +# +# Tomkins, L. M., Yuter, S. E., and Miller, M. A., 2024: Dual adaptive differential +# threshold method for automated detection of faint and strong echo features +# in radar observations of winter storms. Atmos. Meas. Tech., 17, 3377–3399, +# https://doi.org/10.5194/amt-17-3377-2024 diff --git a/pyart/retrieve/cfad.py b/pyart/retrieve/cfad.py index a08c13246d..f85afe7800 100644 --- a/pyart/retrieve/cfad.py +++ b/pyart/retrieve/cfad.py @@ -19,6 +19,10 @@ def create_cfad( histogram that is normalized by the number of points at each altitude. Altitude bins are masked where the counts are less than a minimum fraction of the largest number of counts for any altitude row. + Author: Laura Tomkins (lauramtomkins@gmail.com) + + Parameters + ---------- radar : Radar Radar object used. Can be Radar or Grid object. field_bins : list diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index 327acb9c00..35156c744f 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -326,10 +326,10 @@ def feature_detection( ): """ This function can be used to detect features in a field (e.g. reflectivity, rain rate, snow rate, - etc.) described by Tomkins et al. (2023) and based on original convective-stratiform algorithms developed by + etc.) described by Tomkins et al. (2024) and based on original convective-stratiform algorithms developed by Steiner et al. (1995), Yuter et al. (2005) and Yuter and Houze (1997) algorithm. - Author: Laura Tomkins (@lauratomkins) + Author: Laura Tomkins (lauramtomkins@gmail.com) Parameters ---------- @@ -422,8 +422,10 @@ def feature_detection( 2005: Physical characterization of tropical oceanic convection observed in KWAJEX. J. Appl. Meteor., 44, 385-415. https://doi.org/10.1175/JAM2206.1 - Tomkins, L. M., S. E. Yuter, and M. A. Miller, 2024: Objective identification - of faint and strong features in radar observations of winter storms. in prep. + Tomkins, L. M., Yuter, S. E., and Miller, M. A., 2024: Dual adaptive differential + threshold method for automated detection of faint and strong echo features + in radar observations of winter storms. Atmos. Meas. Tech., 17, 3377–3399. + https://doi.org/10.5194/amt-17-3377-2024 """ diff --git a/pyart/retrieve/qpe.py b/pyart/retrieve/qpe.py index ad8b28856b..934f8d144b 100644 --- a/pyart/retrieve/qpe.py +++ b/pyart/retrieve/qpe.py @@ -127,7 +127,7 @@ def est_rain_rate_kdp(radar, alpha=None, beta=None, kdp_field=None, rr_field=Non rain : dict Field dictionary containing the rainfall rate. - Reference + References --------- Figueras et al. Long-term monitoring of French polarimetric radar data quality and evaluation of several polarimetric quantitative precipitation @@ -696,7 +696,7 @@ def ZtoR(radar, ref_field="reflectivity", a=300, b=1.4, save_name="NWS_primary_p """ Convert reflectivity (dBZ) to precipitation rate (mm/hr) - Author: Laura Tomkins + Author: Laura Tomkins (lauramtomkins@gmail.com) Parameters ---------- diff --git a/pyart/util/radar_utils.py b/pyart/util/radar_utils.py index bc6827e83e..cdead1b1de 100644 --- a/pyart/util/radar_utils.py +++ b/pyart/util/radar_utils.py @@ -613,7 +613,7 @@ def image_mute_radar(radar, field, mute_field, mute_threshold, field_threshold=N the correlation coefficient is less than a certain threshold to discern melting precipitation. - Author: Laura Tomkins (@lauratomkins) + Author: Laura Tomkins (lauramtomkins@gmail.com) Parameters ----------