Search Results

You are looking at 1 - 3 of 3 items for :

  • Author or Editor: Harindra J. S. Fernando x
  • Monthly Weather Review x
  • Refine by Access: All Content x
Clear All Modify Search
Jaynise M. Pérez Valentín
,
Harindra J. S. Fernando
,
G. S. Bhat
,
Hemantha W. Wijesekera
,
Jayesh Phadtare
, and
Edgar Gonzalez

Abstract

The relationship between eastward-propagating convective equatorial signals (CES) along the equatorial Indian Ocean (EIO) and the northward-propagating monsoon intraseasonal oscillations (MISOs) in the Bay of Bengal (BOB) was studied using observational datasets acquired during the 2018 and 2019 MISO-BOB field campaigns. Convective envelopes of MISOs originating from just south of the BOB were associated with both strong and weak eastward CES (average speed ∼6.4 m s−1). Strong CES contributed to ∼20% of the precipitation budget of BOB, and they spurred northward-propagating convective signals that matched the canonical speed of MISOs (1–2 m s−1). In contrast, weak CES contributed to ∼14% of the BOB precipitation budget, and they dissipated without significant northward propagation. Eastward-propagating intraseasonal oscillations (ISOs; period 30–60 days) and convectively coupled Kelvin waves (CCKWs; period 4–15 days) accounted for most precipitation variability across the EIO during the 2019 boreal summer as compared with that of 2018. An agreement could be noted between high moisture content in the midtroposphere and the active phases of CCKWs and ISOs for two observational locations in the BOB. Basin-scale thermodynamic conditions prior to the arrival of strong or weak CES revealed warmer or cooler sea surface temperatures, respectively. Flux measurements aboard a research vessel suggest that the evolution of MISOs associated with strong CES are signified by local enhanced air–sea interactions, in particular the supply of local moisture and sensible heat, which could enhance deep convection and further moisten the upper troposphere.

Significance Statement

Eastward-propagating convective signals along the equatorial Indian Ocean and their relationship to the northward-propagating spells of rainfall that lead to moisture variability in the Bay of Bengal are studied for the 2018 and 2019 southwest monsoon seasons using observational datasets acquired during field campaigns. Strong convective equatorial signals spurred northward-propagating convection, as compared with weak signals that dissipated without significant northward propagation. Wave spectral analysis showed CCKWs (period 4–15 days), and eastward ISOs (period 30–60 days) accounted for most of the precipitation variability, with the former dominating during the 2018 boreal summer. High moisture periods observed from radiosonde measurements show agreement with the active phases of CCKWs and ISOs.

Free access
Robert M. Banta
,
Yelena L. Pichugina
,
W. Alan Brewer
,
Kelly A. Balmes
,
Bianca Adler
,
Joseph Sedlar
,
Lisa S. Darby
,
David D. Turner
,
Jaymes S. Kenyon
,
Edward J. Strobach
,
Brian J. Carroll
,
Justin Sharp
,
Mark T. Stoelinga
,
Joel Cline
, and
Harindra J. S. Fernando

Abstract

Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.

Significance Statement

Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Open access
Robert M. Banta
,
Yelena L. Pichugina
,
W. Alan Brewer
,
Aditya Choukulkar
,
Kathleen O. Lantz
,
Joseph B. Olson
,
Jaymes Kenyon
,
Harindra J. S. Fernando
,
Raghu Krishnamurthy
,
Mark J. Stoelinga
,
Justin Sharp
,
Lisa S. Darby
,
David D. Turner
,
Sunil Baidar
, and
Scott P. Sandberg

Abstract

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

Free access