1. Introduction

In 1996, NCEP developed an hourly, real-time 4km U.S. precipitation analysis (Baldwin and Mitchell, 1997). The analysis uses hourly precipitation estimates from the WSR-88D radar network, and the hourly reports from the approximately 2,500 automated rain gauges via the GOES Data Collection Platform. This product gives us the opportunity to assimilate precipitation observations in the mesoscale Eta model's 4-dimensional data assimilation system (4DDA), in order to improve the quality of
  1. the 4DDA precipitation accuracy
  2. the short-term Eta model precipitation forecast
  3. the other water cycle components, such as soil moisture in the 4DDA and forecast product suite.

2. The Assimilation Method

The essence of precipitation assimilation in the Eta Data Assimilation System (EDAS) is to use observed precipitation to adjust the model's latent heating, moisture and cloud water fields during the 12-hour pre-forecast assimilation period. To that end, at each time step during EDAS, for each grid point where precipitation observations are available, we compare the model precipitation (Pmod ) against the observations (Pobs ) and make adjustments in the following way:
  1. If Pmod > 0 but Pobs = 0: we take back the Pmod and the corresponding amount of latent heating from the model; adjust water vapor mixing ratio (qv) so the relative humidity (RH) remains unchanged; and reduce cloud water mixing ratio to no more than the minimum amount required to produce rain (qcmin).

  2. If Pmod > Pobs > 0: we reduce the latent heat release in each precipitating layer by multiplying the latent heating profile by the factor of Pobs/Pmod; adjust qv as in the previous scenario, and in layers where precipitation is being produced, reduce cloud water proportionally but keep it above the qcmin.

  3. Pmod < Pobs: first check to see if convection is possible, if so, we shorten the convective time scale to accelerate convective precipitation production, with the goal of producing an amount (Pcnv) that equals to Pobs (but no greater than the maximum amount allowed by the convective parameterization).
    After the convective adjustment, if Pcnv < Pobs (either because the profile is not convective, or the maximum possible convective precipitation is less than Pobs), then we perform a grid scale precipitation adjustment by comparing the model's original grid scale precip (Pgrd ) against Pobs - Pcnv.

3. The 1-15 Jul 1998 experiment

In the first half of July 1998, we ran a series of real-time experiments to assimilate the multi-sensor hourly precipitation observations into the 80-km version of the Eta model. The results are compared to a control run, which is an 80-km Eta model that mimicks the 32-km operational Eta model in everyway except the resolution. This series of experiments is later re-run in retrospective mode to further refine the precipitation assimilation method. We find that precipitation assimilation has a significant positive impact on the model's precipitation production during the 12h data assimilation (EDAS) period, as shown below:

Fig. 1 Equitable threat scores (top) and bias scores (bottom) for the precipitation experiment (blue) and the control run (red) during the data assimilation period. The scores are calculated by summing up the 00Z and 12Z cycles of 12h EDAS precipitation (hence yielding a 12Z-12Z 24h sum) and comparing against the 24h sum of multi-sensor hourly observations.

The total precipitation accumulation for the first half of Jul 1998 is shown below, for the two model runs (control, and the precipitation assimilation), and compared to the observed precipitation summed up from the River Forecast Center 24-hour precipitation data:

Fig. 2 Total precipitation accumulation for 12Z 30 Jun - 12Z 15 Jul 1998, from 1) The control run EDAS (top left), 2) the run with precipitation assimilation (top right) and 3) 15-day sum of 24h precipitation observations from the River Forecast Centers.
Compared to observations, the control run has a high precipitation bias in the Southeast U.S., for the first half of Jul 1998. In contrast, the run with precipitation assimilation produced an accumulated rainfall field that is much closer to the observations. The difference in accumulated rainful over the preceding 15 days leads to a large difference in soil moisture by 12Z 15 Jul 1998:

Fig. 3 0-100cm soil moisture availability at 12Z 15 Jul 1998, for 1) the control run (left panel), 2) the run with precipitation assimilation (right panel).

The two series of runs started out with the identical soil moisture fields. In the control run, the large area of wet soil in the southeast region obviously resulted from the excessive rainfall in the area. The more-moderate rainfall in the run with precipitation assimilation produced a more reasonable soil moisture field after 15 days of model run.

We find that with the assimilation of hourly precipitation data, the model's precipitation field during the 12h EDAS period closely approximates the observations. The precipitation assimilation also improves the model's rainfall field during the first several hours of free forecast. It also has a very slight positive impact on longer-term forecasts, though on occasion it produces a markedly better forecast, as shown in Fig. 4.

Fig. 4 24h precipitation accumulation ending 12Z 5 Jul 1998, from the 24h forecast of the two model runs (top two figures), and from the RFC 24h observations (bottom figure)

4. Link to pre-implementation experiments

5. The 24 Jul 2001 Operational Implementation, and beyond

Precipitation assimilation was implemented into the operational Eta model (22km/50 levels) on 24 Jul 2001 along with upgrades to land surface physics and 3DVAR analysis.

On 27 Nov 2001, the Eta model underwent another upgrade, where the grid resolution was increased to 12km/60 levels, a new cloud microphysics package was implemented, and NOAA-16 radiances began to be included in the 3DVAR analysis. The precipitation assimilation also underwent extensive changes at this time to stay in tandem with the new cloud microphysics.

24h Eta Forecast Threat and Bias scores for 2001, before and after the 24 Jul implementation

24h Eta Forecast Threat and Bias scores for 25 Jul-31 Dec, 2000 vs. 2001

6. Discussion

We have found that precipitation assimilation produces a marked improvement on model's atmospheric fields, in both the analysis and forecasts of 24 hours and beyond. It also improves the model's precipitation field significantly during the 12h data assimilation period. This is quite useful in providing an improved soil moisture field for the Eta model: currently there is no real-time accessible nationwide network of soil moisture observations available for assimilation, so an accurate precipitation assimilation can help overcome this void.

The bias scores in Fig. 2 show that, during EDAS, while the precipitation assimilation has corrected the control run's rather high bias in the larger observed precipitation threshold values (0.5 to 2.0 in/day), it tends to under-predict heavier rainfall by 40%-60%. This is largely due to the low bias in the hourly data we use for the assimilation. We use the hourly multi-sensor (largely radar-based) precipitation observations for our assimilation, and evaluate the model's precipitation fields against the 24h RFC observations (largely gauge-based). In the past two years we have routinely compared the multi-sensor hourly observations (summed over 24h periods) against the 24h RFC observations, and found that the multi-sensor observations tend to have false rainfall at very light amounts, while under-estimating heavier rainfall by a percentage similar to that in the EDAS bias scores (Fig. 2).

We plan to use the 24h RFC observations to correct this bias in the hourly data, for the use in the precipitation assimilation. In retrospective mode, this can be done by using the hourly data as ``weights'' to partition the 24h observations and produce a more accurate set of hourly rainfall data. In real time, this can be done by compiling a retrospective database of the bias, in which we compare the two sets of observations over the preceding 30-60 day period, and use this database to perform a bias correction on the real-time hourly precipitation observations.

In the near future, we also plan to include GOES cloud heights observations in the assimilation, to improve the model cloud fields, and to help reduce the ``false light rain'' signal that radar data frequently bring to the hourly precipitation observations.


Support for this research was provided by NOAA via the GEWEX Continental-Scale International Project (GCIP), and by the University Corporation for Atmospheric Research.


Baldwin, M. E., and K.E. Mitchell, 1997: The NCEP hourly multi-sensor U.S. precipitation analysis for operations and GCIP research. Preprints, 13th AMS Conference on Hydrology, Long Beach, CA, 54--55.

Carr, F. H., and M. E. Baldwin, 1991: Assimilation of observed precipitation data using NMC's Eta model. Preprints, 9th AMS Conference on Numerical Weather Prediction, Denver, CO, 422-425.