Understanding and Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System

The accuracy of PBL height simulation is a key issue in many applications including forecasting near surface
meteorology and air quality, however, it is a very challenging problem due to the lack of not
only comprehensive, global Planetary Boundary Layer (PBL) observations but also a strategy and
infrastructure to utilize PBL height data from a variety of sensors. Following the designation of PBL as
an incubation class observable in the 2017 Decadal Survey, the PBL Incubation Study Team Report [14]
made clear that “a future global PBL observing system requires modeling and data assimilation as
essential components.” There is an urgent need for global modeling development in order to utilize
Program of Record (POR) observations, assess their impacts, and identify gaps to be filled by future
PBL missions. Our overall objective is to develop PBL data assimilation capabilities in the NASA
Global Earth Observing System (GEOS), focusing on PBL height from multiple observing systems, to
support the assessment and use of future PBL observations.

The NASA GEOS system is composed of the GEOS global atmospheric general circulation model
(AGCM) and the atmospheric data assimilation system (ADAS). The PBL parameterizations include the
“Lock” K-profile scheme driven by surface and cloud-top buoyancy fluxes ([4]), and the “Louis” local
scheme for stable conditions based on the Richardson number ([5]). Above the mixed layer defined by
the Lock surface plume, shallow cumulus convection is represented by the mass flux scheme of [9].
Additional parameterizations are summarized in [1]. The ADAS employs the hybrid 4D Ensemble-
Variational (EnVar) configuration ([15]), with the ensemble providing flow-dependent background error
covariance information. The resultant analysis increments are fed back to the forecast model through the
4D incremental analysis update (IAU) approach ([11]).

In this study, PBL height data are being or have been generated from radiosondes, GNSS RO, satellite
(CATS, CALIPSO and ICESat-2) and ground-based (MPLNET) lidars, and wind profiler. Investigations
have been conducted to specify quality marks for PBL height retrievals for the data assimilation
purpose. These PBL height data have different strengths and weaknesses ([2], [3], [6], [7], [8], [10]), and
the satellite PBL height data provide better global coverage and complement in-situ PBL height data.
Radiosondes offer high accuracy and in situ measurement of temperature and humidity profiles, but with
poor spatio-temporal sampling. The in-situ observing systems like MPLNET and wind profiler provide
long history of PBL height records at each station. The GNSS RO based PBL height is retrieved based
on the sharp gradients in refractivity profile that represent the fine vertical structure of temperature and
moisture changes above the PBL. However, not all RO refractivity profiles reach the surface depending
on location and regime, and RO refractivity retrievals can be negatively biased below 2km. The PBL
height data from satellite lidars provide high resolution along track PBL height retrievals, but over land
they are affected by previous day convective PBL aerosol and strongly associated with mixing layer and
retrievals cannot be made below thick, attenuating clouds.

A successful assimilation of PBL height data requires a thorough understanding of the observing method
and the retrieval algorithm for each observing system in order to use the PBL height data from multiple
observing systems properly. Due to the sensitivity of PBL height data to the observing method and
choice of algorithm, it is important to use a model definition appropriate for each observation type to
compute differences between PBL height data and model PBL height (OmFs). The GEOS model
currently includes two PBL height definitions suitable for direct comparison with observed PBL height,
and additional definitions are being added in this study. Evaluation of different model PBL height
definitions is underway. Meanwhile, efforts have been made in the GEOS data assimilation system to
develop PBL height data assimilation capability. PBL height data can be assimilated using two different
approaches. The traditional approach is to construct an observation operator and its tangent linear and
adjoint, which link control variables to PBL height data from each observing system. This observation
operator can be very complicated, e.g., the lidar-based PBL height observation operator includes the
backscatter lidar forward observation operator, the algorithm to derive PBL height from attenuated total
backscatter, interpolation, and calculations handling the mismatch between observed and model scales.
The other approach is to augment PBL height to the control variable vector, and it is adopted in this
study. The latter approach was also used in previous studies, e.g., the assimilation of PBL height data
from radiosonde and aircraft in the Real Time Mesoscale Analysis (RTMA) system for a dispersion
modelling study ([13]); the PBL height assimilation study using lidar PBL height data at Greensburg,
Kansas for a field campaign ([12]). The PBL height assimilation from multiple observing systems in this
study allows us to take advantage of the diverse PBL height data that provide much better global
coverage collectively under different meteorological conditions and with different temporal and spatial
scales. As all the PBL heights are tightly coupled with the PBL thermodynamic variables, the strong
correlations, which are provided by the 4D ensemble forecast, enable PBL height data from various
sources to interact and combine coherently and provide additional information for PBL temperature and
moisture fields.

The results of comparisons among PBL height data from different sources and the evaluation of the
model PBL height definitions with the PBL height data will be presented, and the PBL height data
synergy strategies and preliminary results will also be discussed at the conference.