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Title: Towards retrieving critical relative humidity from ground-based remote sensing observations

Abstract

Nearly all parameterisations of large-scale cloud require the specification of the critical relative humidity (RHcrit). This is the gridbox-mean relative humidity at which the subgrid fluctuations in temperature and water vapour become so large that part of a subsaturated gridbox becomes saturated and cloud starts to form. Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered a reasonable estimate of the RHcrit from observations. However, with the advent of ground-based measurements from Raman lidar, it becomes possible to obtain long records of temperature and moisture (co-)variances with sub-minute sample rates. Lidar observations are inherently noisy and any analysis of higher-order moments will be very dependent on the ability to quantify and remove this noise. We present an exporatory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficient to obtain a reasonable estimate of the RHcrit. We show that vertical profiles of RHcrit can be derived for a gridbox length of up to about 30 km (120) with an uncertainty of about 4 % (2 %). RHcrit tends to be smallest near the scale height and seems to be fairly insensitive to the horizontal grid spacing at the scalesmore » investigated here (30 - 120 km). However, larger sensitivity was found to the vertical grid spacing. As the grid spacing decreases from 400 to 100 m, RHcrit is observed to increase by about 6 %, which is more than the uncertainty in the RHcrit retrievals.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1332618
Report Number(s):
PNNL-SA-115884
Journal ID: ISSN 1477-870X; 830403000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Quarterly Journal of the Royal Meteorological Society (Online); Journal Volume: 142; Journal Issue: 700
Country of Publication:
United States
Language:
English

Citation Formats

Van Weverberg, Kwinten, Boutle, Ian, Morcrette, Cyril J., and Newsom, Rob K.. Towards retrieving critical relative humidity from ground-based remote sensing observations. United States: N. p., 2016. Web. doi:10.1002/qj.2874.
Van Weverberg, Kwinten, Boutle, Ian, Morcrette, Cyril J., & Newsom, Rob K.. Towards retrieving critical relative humidity from ground-based remote sensing observations. United States. doi:10.1002/qj.2874.
Van Weverberg, Kwinten, Boutle, Ian, Morcrette, Cyril J., and Newsom, Rob K.. 2016. "Towards retrieving critical relative humidity from ground-based remote sensing observations". United States. doi:10.1002/qj.2874.
@article{osti_1332618,
title = {Towards retrieving critical relative humidity from ground-based remote sensing observations},
author = {Van Weverberg, Kwinten and Boutle, Ian and Morcrette, Cyril J. and Newsom, Rob K.},
abstractNote = {Nearly all parameterisations of large-scale cloud require the specification of the critical relative humidity (RHcrit). This is the gridbox-mean relative humidity at which the subgrid fluctuations in temperature and water vapour become so large that part of a subsaturated gridbox becomes saturated and cloud starts to form. Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered a reasonable estimate of the RHcrit from observations. However, with the advent of ground-based measurements from Raman lidar, it becomes possible to obtain long records of temperature and moisture (co-)variances with sub-minute sample rates. Lidar observations are inherently noisy and any analysis of higher-order moments will be very dependent on the ability to quantify and remove this noise. We present an exporatory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficient to obtain a reasonable estimate of the RHcrit. We show that vertical profiles of RHcrit can be derived for a gridbox length of up to about 30 km (120) with an uncertainty of about 4 % (2 %). RHcrit tends to be smallest near the scale height and seems to be fairly insensitive to the horizontal grid spacing at the scales investigated here (30 - 120 km). However, larger sensitivity was found to the vertical grid spacing. As the grid spacing decreases from 400 to 100 m, RHcrit is observed to increase by about 6 %, which is more than the uncertainty in the RHcrit retrievals.},
doi = {10.1002/qj.2874},
journal = {Quarterly Journal of the Royal Meteorological Society (Online)},
number = 700,
volume = 142,
place = {United States},
year = 2016,
month = 8
}
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