A long-term goal of this work is to find an efficient system for probabilistic planetary boundary
layer (PBL) nowcasting that can be employed wherever surface observations are present. One approach
showing promise is the use of a single column model (SCM) and ensemble filter data assimilation
techniques.
[link to more information]
Dorita Rostkier-Edelstein, rostkier@ucar.edu Josh Hacker, hacker@ucar.edu |

A long-term goal of this work is to find an efficient system for probabilistic planetary
boundary layer (PBL) nowcasting that can be employed wherever surface observations are
present. One approach showing promise is the use of a single column model (SCM) and
ensemble filter data assimilation techniques.

Hacker and Rostkier-Edelstein (2007) showed that surface observations can be an important
source of information with an SCM and an ensemble filter. Here we extend that work to
quantify the probabilistic skill of the SCM with added complexity. Although it is appealing
to add additional physics and dynamics to the SCM model it is not immediately clear that
additional complexity will improve the performance of a PBL nowcasting system based on
a simple model. We address this question with regard to treatment of surface assimilation,
radiation in the column, and also advection to account for realistic 3D dynamics (a timely
WRF prediction). We adopt a factor separation analysis to quantify the individual
contribution of each model component to the probabilistic skill of the system, as well as
any beneficial or detrimental interactions between the different factors.

The probabilistic skill of the system is evaluated through the Brier Skill Score (BSS)
and the area under the relative operating characteristic (ROC) curve (AUR). The BSS is
further decomposed into both a reliability and resolution term to understand the trade-offs
in different components of probabilistic skill. These metrics verify events, and we define an
event here to be a forecast value exceeding the 75th percentile. The climatology of the
observations during the verification period was chosen as reference system.

Results show that assimilation of surface observations can improve skill more significantly
than major model improvements. Figure 1 illustrates some of the probabilistic verification
results for potential temperature profiles estimated at night (0530 UTC, 0030 LT). Black
curves show the values of the metrics obtained for the baseline system. Red curves show
the resulting values when the contribution of a given factor is included. Confidence intervals
(thin dashed curves) were calculated using a bootstrapping technique. Brier reliability and
discrimination are both significantly improved through the lowest few hundred meters when
assimilation is used, but advection is not as successful.

Figure 1: Brier (on 75th percentile observations) reliability term (negative orientation) and area under the ROC (AUR; positively orientation) for potential temperature profiles at night (75th percentile observations). Blackcurves: values skill of baseline system. Red curves: skill including the contribution of a given factor. Dashed curves are corresponding 95% confidence intervals.

Publications:

Hacker, J. P. and D. Rostkier-Edelstein, 2007: PBL state estimation with surface
observations, a column model, and an ensemble filter. Mon. Wea. Rev., 135, 2958-2972