vertical structure, melting layer heights, and antecedent upstream air trajectories associated with...

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Vertical Structure, Melting Layer Heights, and Antecedent Upstream Air Trajectories Associated with Precipitation Events in the Central Andes of Peru Eric J. Burton 1 L. Baker Perry 1 , Marti Bonshoms 2 , Guido Mamani 2 , Nelson Quispe 2 1 Department of Geography and Planning, Appalachian State University, USA 2 Servicio Nacional de Meteorología e Hidrología (SENAMHI), Perú Introduction The Central Andean region of Southern Peru is of great climatological importance. The surrounding area contains tropical glaciers and ice caps that are significant sites for climate, glacier, and paleoclimate research. The aim of this research is to characterize the vertical structure, melting layer heights, cloud top temperatures, and backward air trajectories for precipitation events in Cusco, Peru during the 2014-2015 wet season. Results and Discussion Analysis of the MRR data, GOES IR data, and HYSPLIT backward air trajectories suggests the following: There are two peaks in maximum Doppler velocity, one between 7 and 7.5 m/s, and the other between 10 and 10.5 m/s. The majority of maximum dBZ values fall between 21 and 30, with the ranges from 27 to 33 being the most frequent. Nighttime events are inferred to be stratiform in nature, have longer durations, well-defined melting layers between 4000 and 5000m, and variable echo top heights, which are often lower than those of daytime events. Shorter, more intense events have greater vertical development, usually over 7000m asl, and are inferred to often be convective in nature. Longer events often have stratiform characteristics and echo top heights of 6000 m asl or lower. Trajectories associated with precipitation events were primarily from the NW. Precipitation generally occurs at night with melting layer heights as low as 4000m asl. Lower melting layer heights are often associated with higher dBZ and Doppler velocity values, signifying more intense precipitation. Precipitation occurs with a wide variety of cloud top temperatures but most commonly in the -22 to -6°C range. Air parcels with the shortest 72-hr. backward air trajectories often travel closer to the surface. Figure 1a. dBZ and Doppler velocity from 22:00 1/10/15 – 10:00 1/11/15. Figure 1b. 72-hr. backward air trajectory from 06:00 1/11/15. Acknowledgements The authors are grateful to NOAA’s Air Resources Laboratory for the use of their HYSPLIT model, SENAMHI Peru, Sandra Yuter, Chris Pawlyszyn, and the National Science Foundation for funding through Grant AGS-1347179 (CAREER: Multiscale Investigations of Tropical Andean Precipitation). Figure 4a. dBZ and Doppler velocity from 00:00 – 12:00 10/08/14. Figure 4b. 72-hr. backward air trajectory from 3:00 10/08/14. Study Area, Instrumentation, and Methods We used data from a vertically pointing Micro Rain Radar installed in Cusco, Peru (3350m), near the Cordillera Vilcanota during the period August 2014 to February 2015. The radar measures reflectivity (dBZ) and Doppler velocity (m s - 1 ), allowing for the determination of echo top heights and melting layer heights. GOES IR temperature data from SENAMHI were used to analyze cloud top temperatures. The National Oceanic and Figure 5. A map of the Cusco region of Peru showing elevation and the city of Cusco in relation to the Cordillera Vilcanota. Figure 2a. dBZ and Doppler velocity from 21:00 1/26/15 – 9:00 1/27/15. Figure 3a. dBZ and Doppler velocity from 16:00 2/11/15 – 9:00 2/12/15. Figure 2b. 72-hr. backward air trajectory from 6:00 1/27/15. Figure 3b. 72-hr. backward air trajectory from 06:00 2/12/15. Summary Figure 8a. Summary graph showing frequency of maximum Doppler velocity values. Figure 8b. Summary graph showing frequency of maximum dBZ values. Figure 8c. Summary graph showing frequency of melting layer heights. Figure 8d. Summary graph showing frequency of cloud top temperatures during precipitation events. Atmospheric Administration’s Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was used to run 72-hr. backward air trajectories for precipitation events in the region and clusters ending at 4000 and 6000m asl. 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 0 10 20 30 40 50 60 70 Maximum Doppler Velocity (m s-1) # of hours 4200 4400 4600 4800 5000 >5000 0 10 20 30 40 50 60 70 80 90 Melting Layer Height (m asl) # of hours 15 18 21 24 27 30 33 36 39 42 45 48 51 0 10 20 30 40 50 60 70 80 90 Maximum dBZ # of hours Snow Melting Layer Rain Snow Melting Layer Rai n -71 -54 -38 -22 -6 10 27 0 10 20 30 40 50 60 70 80 90 Temperature (°C) # of Hours Figure 6a. Mean 72 hr. backward air trajectories ending at 4000m asl. Figure 7a. Mean 72 hr. backward air trajectories ending at 6000m asl. Figure 7b. Vertical profiles of mean 72 hr. backward air trajectories ending at 6000m asl. Figure 6b. Vertical profiles of mean 72 hr. backward air trajectories ending at 4000m asl. Cluster 4 11% of events Cluster 1 54% of Events Cluster 2 22% of Events Cluster 5 5% of Events Cluster 6 3% of Events Cluster 3 5% of Events Cluster 6 10% of Events Cluster 5 12% of Events Cluster 4 16% of Events Cluster 2 6% of Events Cluster 1 16% of Events Cluster 3 41% of Events 0 12 24 36 48 60 72 0 500 1000 1500 2000 2500 3000 3500 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Hours Meters AGL 0 12 24 36 48 60 72 0 500 1000 1500 2000 2500 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Hours Meters AGL

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Page 1: Vertical Structure, Melting Layer Heights, and Antecedent Upstream Air Trajectories Associated with Precipitation Events in the Central Andes of Peru Eric

Vertical Structure, Melting Layer Heights, and Antecedent Upstream Air Trajectories Associated with Precipitation Events in the Central Andes of Peru

Eric J. Burton1 L. Baker Perry1, Marti Bonshoms2, Guido Mamani2, Nelson Quispe2

1Department of Geography and Planning, Appalachian State University, USA2Servicio Nacional de Meteorología e Hidrología (SENAMHI), Perú

Introduction

The Central Andean region of Southern Peru is of great climatological importance. The surrounding area contains tropical glaciers and ice caps that are significant sites for climate, glacier, and paleoclimate research. The aim of this research is to characterize the vertical structure, melting layer heights, cloud top temperatures, and backward air trajectories for precipitation events in Cusco, Peru during the 2014-2015 wet season.

Results and DiscussionAnalysis of the MRR data, GOES IR data, and HYSPLIT backward air trajectories suggests the following:

• There are two peaks in maximum Doppler velocity, one between 7 and 7.5 m/s, and the other between 10 and 10.5 m/s.

• The majority of maximum dBZ values fall between 21 and 30, with the ranges from 27 to 33 being the most frequent.

• Nighttime events are inferred to be stratiform in nature, have longer durations, well-defined melting layers between 4000 and 5000m, and variable echo top heights, which are often lower than those of daytime events.

• Shorter, more intense events have greater vertical development, usually over 7000m asl, and are inferred to often be convective in nature.

• Longer events often have stratiform characteristics and echo top heights of 6000 m asl or lower.

• Trajectories associated with precipitation events were primarily from the NW.• Precipitation generally occurs at night with melting layer heights as low as

4000m asl.• Lower melting layer heights are often associated with higher dBZ and Doppler

velocity values, signifying more intense precipitation.• Precipitation occurs with a wide variety of cloud top temperatures but most

commonly in the -22 to -6°C range.• Air parcels with the shortest 72-hr. backward air trajectories often travel closer

to the surface.

Figure 1a. dBZ and Doppler velocity from 22:00 1/10/15 – 10:00 1/11/15.

Figure 1b. 72-hr. backward air trajectory from 06:00 1/11/15.

AcknowledgementsThe authors are grateful to NOAA’s Air Resources Laboratory for the use of their HYSPLIT model, SENAMHI Peru, Sandra Yuter, Chris Pawlyszyn, and the National Science Foundation for funding through Grant AGS-1347179 (CAREER: Multiscale Investigations of Tropical Andean Precipitation).

Figure 4a. dBZ and Doppler velocity from 00:00 – 12:00 10/08/14. Figure 4b. 72-hr. backward air trajectory from 3:00 10/08/14.

Study Area, Instrumentation, and Methods

We used data from a vertically pointing Micro Rain Radar installed in Cusco, Peru (3350m), near the Cordillera Vilcanota during the period August 2014 to February 2015. The radar measures reflectivity (dBZ) and Doppler velocity (m s-1), allowing for the determination of echo top heights and melting layer heights. GOES IR temperature data from SENAMHI were used to analyze cloud top temperatures. The National Oceanic and

Figure 5. A map of the Cusco region of Peru showing elevation and the city of Cusco in relation to the Cordillera Vilcanota.

Figure 2a. dBZ and Doppler velocity from 21:00 1/26/15 – 9:00 1/27/15.

Figure 3a. dBZ and Doppler velocity from 16:00 2/11/15 – 9:00 2/12/15.

Figure 2b. 72-hr. backward air trajectory from 6:00 1/27/15.

Figure 3b. 72-hr. backward air trajectory from 06:00 2/12/15.

Summary

Figure 8a. Summary graph showing frequency of maximum Doppler velocity values.

Figure 8b. Summary graph showing frequency of maximum dBZ values.

Figure 8c. Summary graph showing frequency of melting layer heights.

Figure 8d. Summary graph showing frequency of cloud top temperatures during precipitation events.

Atmospheric Administration’s Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was used to run 72-hr. backward air trajectories for precipitation events in the region and clusters ending at 4000 and 6000m asl.

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.50

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Figure 6a. Mean 72 hr. backward air trajectories ending at 4000m asl.

Figure 7a. Mean 72 hr. backward air trajectories ending at 6000m asl.

Figure 7b. Vertical profiles of mean 72 hr. backward air trajectories ending at 6000m asl.

Figure 6b. Vertical profiles of mean 72 hr. backward air trajectories ending at 4000m asl.

Cluster 4 11% of events

Cluster 154% of Events

Cluster 222% of Events

Cluster 55% of Events

Cluster 63% of Events

Cluster 35% of Events

Cluster 610% of Events

Cluster 512% of Events

Cluster 416% of Events

Cluster 26% of Events

Cluster 116% of Events

Cluster 341% of Events

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Cluster 1 Cluster 2 Cluster 3Cluster 4 Cluster 5 Cluster 6

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