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  • View in gallery

    Wavenumber–frequency diagrams for the 20–100-day bandpass-filtered 200-hPa zonal winds averaged over 10°S–10°N for (from left to right) the ERAI, NOGAPS, and GFS analyses in boreal summer during 2004–10.

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    (left) The 20–100-day bandpass-filtered NOAA interpolated daily outgoing longwave radiation (OLR; contours) and 850-hPa zonal winds from ERAI (shading) for (from top to bottom) eight MJO phases (by WH04) in boreal summer during 2004–10. The red (blue) contours outline the positive (negative) 6 and 18 W m−2 filtered OLR. (middle),(right) As in (left), but just for the 850-hPa zonal winds from the NOGAPS and GFS analyses, respectively. Spatial pattern correlation coefficients of the zonal winds at 850 hPa (200 hPa) between the NOGAPS–GFS analysis and ERAI at eight MJO phases are marked above each plot. The numbers of composite days for each phase are indicated to the right of each row.

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    (top three rows) Patterns of evolution of RH (%) anomalies from 25 days before to 15 days after the MJO peak convection over the Indian Ocean (10°S–10°N, 50°–100°E), derived from the ERAI, NOGAPS, and GFS analyses, respectively. (bottom) Time series of the space–time-filtered OLR during the same period. Day 0 refers to the peak convection day, and positive (negative) lags indicate the time after (before) the peak convection day.

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    As in Fig. 3, but for the diabatic heating rate (Q1). Unit is K day−1.

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    The average daily precipitation rate (mm h−1) in the tropics (15°S–30°N, 180°–180°) using all available data in boreal summer during 2008–11 for (a) CMORPH, (b) NOGAPS 6-day forecasts, (c) the difference between (b) and (a); (d) difference between the NOGAPS 6- and 2-day forecasts; and (e)–(g) as in (b)–(d), but for the GFS.

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    The average hourly precipitation rate (mm h−1) over the eastern Pacific ITCZ region (0°–25°N, 100°–85°W) with different forecast lead times using all available data in boreal summer during 2008–11: (a) the NOGAPS forecast (colors) and CMORPH (black); and (b) GFS (colors) and CMORPH (black).

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    Vertical cross sections of the zonally averaged diabatic heating rate (Q1; K day−1) using all available data in boreal summer during 2008–11 for (from left to right) ERAI and NOGAPS 3- and 5-day forecasts over the (top) western Pacific (120°E–180°), (middle) eastern Pacific (115°–85°W), and (bottom) Indian Ocean (50°–80°E).

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    Vertical cross sections of (top),(middle) the zonally averaged zonal (U) and (bottom) meridional (V) winds using all available data in boreal summer during 2008–11 for (left) ERAI, (middle) the NOGAPS 3-day forecast minus ERAI, and (right) the NOGAPS 5-day minus the NOGAPS 3-day forecasts over the (top) western Pacific (120°–150°E), (middle) eastern Pacific (115°–85°W), and (bottom) Indian Ocean (50°–70°E). Unit is m s−1.

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    The average daily precipitation rate (mm day−1) stratified with 1-mm-wide bins of the CWV (mm) using all available data over four tropical ocean basins (20°S–20°N) in boreal summer during 2008–11. Shown are the curves for the (a) Indian Ocean (30°–120°E), (b) western Pacific (120°E–180°), (c) eastern Pacific (180°–80°W), and (d) Atlantic (80°W–30°E). The black solid lines indicate the SSM/IS precipitation rate vs the SSM/IS CWV; black dotted lines show the CMORPH precipitation rate vs the ERAI CWV; red, green, and blue lines show the 1-, 3-, and 5-day precipitation rates, respectively, of the NOGAPS forecast vs the 1-, 3-, and 5-day CWVs of the NOGAPS forecast.

  • View in gallery

    (a) PDFs (log10%) of the average CMORPH precipitation rate over the tropical ocean basins (20°S–20°N) stratified into 15-mm-wide bins. (b) The relative PDF differences of precipitation rate (%) over the tropical ocean basins between the NOGAPS 3-day forecast and CMORPH. (c) As in (b), but for differences between the NOGAPS 5- and 3-day forecasts. The rainfall types are defined according to the classifications in the Glossary of Meteorology: drizzle (0–12 mm day−1), light (13–60 mm day−1), moderate (61–182 mm day−1), and heavy (>182 mm day−1).

  • View in gallery

    As in Fig. 9, but for the CWV probability distribution (%). The black solid (dotted) lines show the SSM/IS (ERAI) CWV; the faint yellow lines are for the NOGAPS analysis; and the red, green, and blue lines show the 1-, 3-, and 5-day NOGAPS forecasts, respectively.

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    The RH (%) stratified with 1-mm-wide bins of the CWV (mm) using data in boreal summer during 2008–11 over all the tropical ocean basins (20°S–20°N, 180°–180°), from (a) ERAI; (b) the NOGAPS 3-day forecast; (c) the relative percentage difference between the NOGAPS 3-day forecast and ERAI; and (d) as in (c), but for the difference between the NOGAPS 5- and 3-day forecasts.

  • View in gallery

    As in Fig. 12, but for Q1. Unit is K day−1.

  • View in gallery

    Spatial pattern correlations of tropical (20°S–20°N, 180°–180°) zonal winds at 200 hPa between forecasts from: (a) the NOGAPS and ERAI and (b) GFS and ERAI for eight MJO phases (WH04), using all available data in boreal summer during 2008–11. The red lines of decreasing thicknesses show the correlations for 1- to 6-day forecasts.

  • View in gallery

    (a) Composite anomalies of RH (%) for five MJO events during 2008–11 derived from the NOGAPS 3-day forecasts with respect to the daily average of the NOGAPS analysis during 2004–10. (b) As in (a), but for Q1 (K day−1). (c) The RH profiles (%) on day 0 from ERAI (black), the NOGAPS analysis (faint yellow), and the 3-day (green) and 5-day (blue) NOGAPS forecast. (d) As in (c), but for Q1 (K day−1).

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Evaluation of Tropical Intraseasonal Variability and Moist Processes in the NOGAPS Analysis and Short-Term Forecasts

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  • 1 University of Illinois at Urbana–Champaign, Urbana, Illinois
  • | 2 Marine Meteorology Division, Naval Research Laboratory, Monterey, California
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Abstract

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational forecasts are evaluated against the Interim ECMWF Re-Analysis (ERA-Interim; ERAI) and satellite data, and compared with the Global Forecast System (GFS) analysis and forecasts, using both performance- and physics-based metrics. The NOGAPS analysis captures realistic Madden–Julian oscillation (MJO) signals in the dynamic fields and the low-level premoistening leading to active convection, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in the ERAI or the GFS analysis. The NOGAPS forecasts, similar to the GFS forecasts, have relatively low prediction skill for the MJO when the MJO initiates over the Indian Ocean and when active convection is over the Maritime Continent. The NOGAPS short-term precipitation forecasts are broadly consistent with the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation results with regionally quantitative differences. Further evaluation of the precipitation and column water vapor (CWV) indicates that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV, and the NOGAPS forecasts show a dry bias in the CWV increasing with forecast lead time. The NOGAPS underpredicts light and moderate-to-heavy precipitation but overpredicts extremely heavy rainfall. The vertical profiles of RH and Q1 reveal a dry bias within the marine boundary layer and a moist bias above. The shallow heating mode is found to be missing for CWV < 50 mm in the NOGAPS forecasts. The diabatic heating biases are associated with weaker trade winds, weaker Hadley and Walker circulations over the Pacific, and weaker cross-equatorial flow over the Indian Ocean in the NOGAPS forecasts.

Corresponding author address: Weiwei Li, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory St., Urbana, IL 61801. E-mail: wli16@illinois.edu

Abstract

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational forecasts are evaluated against the Interim ECMWF Re-Analysis (ERA-Interim; ERAI) and satellite data, and compared with the Global Forecast System (GFS) analysis and forecasts, using both performance- and physics-based metrics. The NOGAPS analysis captures realistic Madden–Julian oscillation (MJO) signals in the dynamic fields and the low-level premoistening leading to active convection, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in the ERAI or the GFS analysis. The NOGAPS forecasts, similar to the GFS forecasts, have relatively low prediction skill for the MJO when the MJO initiates over the Indian Ocean and when active convection is over the Maritime Continent. The NOGAPS short-term precipitation forecasts are broadly consistent with the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation results with regionally quantitative differences. Further evaluation of the precipitation and column water vapor (CWV) indicates that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV, and the NOGAPS forecasts show a dry bias in the CWV increasing with forecast lead time. The NOGAPS underpredicts light and moderate-to-heavy precipitation but overpredicts extremely heavy rainfall. The vertical profiles of RH and Q1 reveal a dry bias within the marine boundary layer and a moist bias above. The shallow heating mode is found to be missing for CWV < 50 mm in the NOGAPS forecasts. The diabatic heating biases are associated with weaker trade winds, weaker Hadley and Walker circulations over the Pacific, and weaker cross-equatorial flow over the Indian Ocean in the NOGAPS forecasts.

Corresponding author address: Weiwei Li, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S. Gregory St., Urbana, IL 61801. E-mail: wli16@illinois.edu

1. Introduction

The Madden–Julian oscillation (MJO) is the dominant intraseasonal mode in the tropics (Madden and Julian 1972, 1994; Lau and Waliser 2005; Wang 2005). It has significant impacts on the Asian and Australian monsoons as well as the North American and South American monsoons (Yasunari 1979; Lau and Chan 1986; Kiladis and Weickmann 1992; Mo 2000; Higgins et al. 2000). It plays an active role in the onset and development of ENSO (e.g., Zhang and Gottschalck 2002) and also modulates tropical convection and tropical cyclone activities over the eastern Pacific and the Atlantic (e.g., Nakazawa 1988; Chen et al. 1996; Hendon and Salby 1994; Maloney and Hartmann 2000a,b). As a low-frequency mode, the MJO serves as an additional source of predictability, besides the lower boundary forcing (SST and land surface conditions), for the atmosphere on subseasonal time scales (e.g., Leroy and Wheeler 2008; Vitart and Molteni 2010; Fu and Hsu 2011). Previous studies have shown that the MJO has remote impacts on the extratropics and modulates the variability and predictability of the midlatitude weather systems (e.g., Liebmann and Hartmann 1984; Weickmann et al. 1985; Lau and Phillips 1986; Jones et al. 2004). Realistic simulations of the MJO and its associated teleconnection patterns in a global model are thus important for both weather forecasts and seasonal prediction.

Model intercomparison studies (e.g., Slingo et al. 1996; Lin et al. 2006) have shown that general circulation models (GCMs) often have difficulty in simulating the MJO with realistic propagation speed and amplitude. Although the exact reason for these deficiencies is not clear, it is generally believed that inadequate representation of cloud processes and multiscale interactions is likely the major culprit (e.g., Tokioka et al. 1988; Wang 2005). As an intrinsic multiscale system (e.g., Majda and Biello 2004; Zhang et al. 2010), the MJO serves as an excellent benchmark for evaluating model parameterizations across different spatial and temporal scales. In the present study, an evaluation of the MJO-associated tropical intraseasonal variability in the analyses and short-term weather forecasts of a global model is presented, in principle offering a look at the development of model forecast errors before the atmospheric state diverges too far from the reality. An examination of more general characteristics of moist processes in the tropics is also presented in an attempt to gain insight into the deficiencies of the model physics on the intraseasonal time scales.

The forecast system chosen for evaluation in this study is the Navy Operational Global Atmospheric Prediction System (NOGAPS). NOGAPS, developed at the Naval Research Laboratory (NRL), had been running operationally at the U.S. Navy’s Fleet Numerical Meteorology and Oceanography Center (FNMOC) since 1982, and was replaced with the Navy Global Environmental Model (NAVGEM) in March 2013. NOGAPS produced 6-day forecasts twice daily and, in addition, provided forcing and/or initial and boundary conditions to many other models, including the Navy’s advanced Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS1), an ocean wave model, a sea ice model, an ocean circulation model, an ocean thermodynamics model, a tropical cyclone model, and application programs at both FNMOC and the Air Force Weather Agency (AFWA). The products were also used at the Joint Typhoon Warning Center (JTWC) and the National Hurricane Center (NHC) for tropical cyclone forecasts. Modifications and updates to the model through the years were stringently tested by the NRL and FNMOC before being adopted into operation, but the evaluations mainly focused on the prediction skills of short-term weather forecasts. In this study, we will evaluate the representation of tropical intraseasonal variability and moist processes in the NOGAPS operational analysis and forecasts systematically using a suite of diagnostic tools with both performance- and physics-based metrics. The NOGAPS evaluation will address the following three aspects with a special focus on the relevant physical processes: (i) the mean states, (ii) the MJO, and (iii) precipitation and moist processes. It is expected that this study will help to guide the diagnostic evaluation of the successor forecast system, NAVGEM, as the development of U.S. Navy atmospheric prediction extends its focus toward longer lead times.

This paper is organized as follows. A description of NOGAPS, in particular the forecast model and its treatment of moist processes, is presented in section 2. The data and methods are described in section 3. The MJO in the NOGAPS analysis is examined against the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim; ERAI) and compared with the Global Forecast System (GFS) analysis in section 4. The NOGAPS short-term forecasts are evaluated in section 5, including the biases in the thermodynamic fields and their impacts on large-scale atmospheric circulations and the MJO forecast skill. Section 6 offers the summary and discussion.

2. NOGAPS description

The NOGAPS forecast system was upgraded to version 4.1 in 2002. It ran operationally at a horizontal resolution of T239 (equivalent to approximately 50 km at the equator) with 30 vertical levels. By the time it was removed from operational use in 2013, the horizontal resolution had been increased to T319, and the model had 42 vertical levels. The NOGAPS data assimilation system in 2002 was a multivariate optimal interpolation (MVOI) analysis scheme. This was replaced in September 2003 by the NRL Atmospheric Variational Data Assimilation System (NAVDAS), and in September 2009 by the NAVDAS-Accelerated Representer (NAVDAS-AR) four-dimensional variational data assimilation (4DVAR) scheme. The physics schemes in the NOGAPS model had been evaluated and updated over the years, particularly the treatment of convection. The convective scheme of Arakawa and Schubert (1974), discretized following Lord (1982), was used until being replaced by a “relaxed” Arakawa–Schubert scheme similar to that described by Moorthi and Suarez (1992). In 2002, the Emanuel convection scheme was adopted. The original Emanuel scheme yielded improvements to tropical cyclone track forecasts, but was found to underpredict heavy precipitation events, overpredict light precipitation, and have unrealistic heating at upper levels. A modified treatment by Peng et al. (2004) was then adopted. The Emanuel scheme was further improved by increasing the momentum mixing, yielding significant improvement in tropical cyclone track forecasts (Hogan and Pauley 2007). Cloud fractions for deep convective clouds were estimated by a diagnostic cloud scheme (Slingo 1987). Teixeira and Hogan (2002) implemented a new cloud fraction scheme for shallow clouds, which improved the global distributions of the boundary layer clouds and surface shortwave radiation in the model. The adoption of the NAVDAS-AR 4DVAR system, the use of radiance data, and the increase in resolution from T239 to T319 have all contributed to improvements in prediction skill in recent years.

3. Data and method

a. Data

The NOGAPS analysis and operational forecasts are evaluated against ERAI and compared with the GFS analysis and operational forecasts. ERAI is the latest major undertaking of the ECMWF (Dee et al. 2011). It utilizes 4DVAR and provides a more realistic representation of the atmosphere in both space and time than did earlier versions of the reanalysis. All the datasets are regridded to 1.0° × 1.0° resolution, and daily averages are derived from 6-hourly data. The NOGAPS analysis was evaluated for May–November during 2004–10. We focus on the boreal summer because this is the season during which the NOGAPS forecasts are available. The NOGAPS 1–7-day forecasts are available for the following time periods: 7 September–28 November 2008, 14 June–31 October 2009, 4 June–5 November 2010, and 8 July–15 October 2011. Reanalysis and satellite data in the same periods were used to evaluate the NOGAPS forecasts.

Two satellite datasets were employed to evaluate precipitation and moist processes. The Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation product is a 3-hourly global precipitation dataset with a spatial resolution of 0.25° × 0.25° (Joyce et al. 2004). Precipitation was estimated from passive microwave precipitation retrievals, and motion vectors derived from geostationary satellite IR data were used to advect the precipitation features. CMORPH thus better represents the spatial structure of precipitation compared to Tropical Rainfall Measuring Mission (TRMM) 3B42. To be consistent with the analysis and forecast data, daily means were derived from the CMORPH precipitation and regridded to the 1.0° × 1.0° resolution. Column water vapor (CWV) and precipitation from the version 7 Special Sensor Microwave Imager Sounder (SSM/IS) polar-orbiting dataset were used to evaluate the precipitation–CWV relationship. The SSM/IS data have a spatial resolution of 0.25° × 0.25°, using a unified and physically based algorithm to simultaneously retrieve precipitation and CWV (Wentz and Spencer 1998; Horváth and Gentemann 2007). The daily average over the ocean was derived from the F-16 and F-17 satellites for the same periods as the NOGAPS forecasts and interpolated to 1.0° × 1.0° resolution.

b. The MJO indices and the MJO metrics

To evaluate the MJO in the global model analyses and forecasts, we adopted the standardized set in the MJO diagnostics package developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (Kim et al. 2009). It objectively evaluates global model simulations of the MJO within a consistent framework (CLIVAR Madden–Julian Oscillation Working Group 2009). Both “global” and “local” MJO indices in this study were used to select the MJO events and construct composites. For the global MJO index, we employed the “All-season Real-time Multivariate MJO Index” (RMM; Wheeler and Hendon 2004, hereafter WH04). The RMM was derived from the multivariable empirical orthogonal functions of outgoing longwave radiation (OLR) and 850- and 200-hPa zonal winds, and was downloaded online (http://cawcr.gov.au/staff/mwheeler/maproom/RMM). The RMM emphasizes the global structure of the MJO. To examine the MJO-related variability over specific regions, we defined a local MJO index. A space–time filter was applied to the meridionally averaged (10°S–10°N) daily OLR to extract variations with zonal wavenumbers 0–10 and periods between 30 and 91 days (or 4–12 cpy). The data were then normalized by the standard deviation at each grid point. The resultant two-dimensional (longitude and time) OLR array was used to define a local MJO index for a given longitude or longitude range. “Day 0” refers to the peak convection day, when the normalized OLR anomaly reaches a negative minimum and its magnitude exceeds 1.0. Positive (negative) lags indicate the time after (before) the peak convection.

4. Performance of the NOGAPS analysis in describing the MJO

As the first step, the MJO signals in the NOGAPS analysis were evaluated against ERAI and compared to the GFS analysis. Figure 1 shows the wavenumber–frequency diagrams of 200-hPa zonal wind averaged over 10°S–10°N from the ERAI, NOGAPS, and GFS analyses. The annual cycle was first removed by subtracting the climatological mean daily data, and then a 20–100-day bandpass filter (Duchon 1979) was used to extract the intraseasonal variations prior to the wavenumber–frequency analysis. The NOGAPS analysis captures the MJO signals with a realistic frequency range and spatial scales, that is, eastward-propagating signals are stronger than westward ones, with the maximum power spectra at globally zonal wavenumber 1 and the dominant periods between 40 and 50 days. However, it is discernible that the MJO spectral power in the NOGAPS analysis is weaker than that in the GFS or ERAI simulations. The NOGAPS analysis also slightly overestimates the variances at global wavenumbers 2 and 3.

Fig. 1.
Fig. 1.

Wavenumber–frequency diagrams for the 20–100-day bandpass-filtered 200-hPa zonal winds averaged over 10°S–10°N for (from left to right) the ERAI, NOGAPS, and GFS analyses in boreal summer during 2004–10.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

The MJO has a baroclinic vertical structure. Convection is closely coupled to the upper- and low-level wind fields over the Indo-Pacific warm pool region. The composites of 20–100-day bandpass-filtered 850-hPa zonal winds were constructed for different MJO phases based on the RMM (Fig. 2). To extract prominent MJO events, we only selected the days when the “MJO amplitude” (WH04) exceeded 1.0 (the number of the composite days is indicated to the right of each row). The composites derived from the National Oceanic and Atmospheric Administration (NOAA) daily OLR and the ERAI 850-hPa zonal wind are shown in the left column of Fig. 2 to serve as benchmarks for the evaluation, and also to provide a large-scale perspective of the MJO evolution. In phase 1 of the MJO, the MJO convection decays over the central Pacific and is initiated over the Indian Ocean. The easterly anomalies are over the North Indian Ocean and the Maritime Continent. In phase 2, as convection develops over the north Indian Ocean, the easterly anomalies are weakened. In phase 3, enhanced convection is over the Indian Ocean and extends eastward to the Maritime Continent. Meanwhile, the low-level westerlies develop over the central and eastern Indian Ocean, nearly collocated with convection, and the easterly anomalies over the Maritime Continent are also strengthened. From phase 4 to phase 6, convection and westerly anomalies move eastward to the Maritime Continent and then to the western Pacific. A pattern of northward propagation is evident in both fields, which is consistent with previous studies (Kemball-Cook and Wang 2001; Wheeler and Hendon 2004; Yang et al. 2013). In phase 7, convection and westerly anomalies are weakened over the western Pacific. The easterly anomalies start prevailing over the Indian Ocean. In phase 8, convection is suppressed (enhanced) over the Maritime Continent and the western (eastern) Pacific. Widespread westerly anomalies cover the tropical Pacific.

Fig. 2.
Fig. 2.

(left) The 20–100-day bandpass-filtered NOAA interpolated daily outgoing longwave radiation (OLR; contours) and 850-hPa zonal winds from ERAI (shading) for (from top to bottom) eight MJO phases (by WH04) in boreal summer during 2004–10. The red (blue) contours outline the positive (negative) 6 and 18 W m−2 filtered OLR. (middle),(right) As in (left), but just for the 850-hPa zonal winds from the NOGAPS and GFS analyses, respectively. Spatial pattern correlation coefficients of the zonal winds at 850 hPa (200 hPa) between the NOGAPS–GFS analysis and ERAI at eight MJO phases are marked above each plot. The numbers of composite days for each phase are indicated to the right of each row.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

The composites of filtered 850-hPa zonal wind from the NOGAPS and GFS analyses are shown in the two right columns of Fig. 2. Both analyses show a close resemblance with the ERAI. The northward propagation of the westerly anomalies over the western Pacific is captured by both analyses. The spatial correlations of the NOGAPS with the ERAI between 20°S and 20°N remain above 0.97 in all the MJO phases. Similarly strong pattern correlations are also found in the 200-hPa zonal wind. Overall, Figs. 1 and 2 show that the MJO signals in the dynamic fields of the NOGAPS and GFS analyses are in good agreement with those in the ERAI. The NOGAPS analysis performs better in the lower troposphere, whereas the GFS analysis is somewhat better than the NOGAPS in the upper troposphere, as indicated by the spatial correlations.

Previous studies have revealed the complex vertical structure of the MJO in the moisture field: low-level moistening precedes the peak convection and enhanced moisture occurs throughout the troposphere at the time of the peak convection, which is followed by drying in the lower troposphere (Kemball-Cook and Wang 2001; Kiladis et al. 2005; Tian et al. 2010). The vertical structure of moisture anomalies is consistent with the cloud transition associated with the MJO (Benedict and Randall 2007; Jiang et al. 2011; Del Genio et al. 2012), and is believed to be critical for the evolution and propagation of the MJO (e.g., Zhao et al. 2013). To examine whether this feature is captured by the analyses, the composites of unfiltered daily relative humidity (RH) were constructed using the local MJO index defined over the Indian Ocean (10°S–10°N, 50°E–100°E). The daily climatology derived from the 2004–10 analysis was first removed to exclude the annual cycle. The Indian Ocean was chosen here to examine the premoistening process associated with the MJO initiation. Nineteen prominent MJO events in the boreal summer during 2004–10 were selected.

As shown in Fig. 3, the ERAI clearly illustrates the evolution of the moisture field. The lower-tropospheric premoistening starts more than 10 days before the peak convection. As the peak convection approaches, the RH increases throughout the troposphere, with a maximum anomaly of up to 9% around 450 hPa. The upper-tropospheric moistening persists more than 10 days after the peak convection while the lower-to-middle troposphere dries up earlier, owing to either the stratiform precipitation or the horizontal advection or both (e.g., Chikira 2014; Maloney and Hartmann 1998; Benedict and Randall 2007). The GFS analysis resembles the ERAI except that a weak premoistening occurs more than 20 days prior to the peak convection. Although the NOGAPS captures the premoistening signals, the RH anomalies are about 50% weaker than those in the ERAI or GFS, and the maximum moistening slightly lags the peak convection. A thin layer of negative RH anomalies appears on day −7 and onward around 850 hPa. A similar low-level dry bias is also found in the NOGAPS forecasts and will be discussed in detail in section 5.

Fig. 3.
Fig. 3.

(top three rows) Patterns of evolution of RH (%) anomalies from 25 days before to 15 days after the MJO peak convection over the Indian Ocean (10°S–10°N, 50°–100°E), derived from the ERAI, NOGAPS, and GFS analyses, respectively. (bottom) Time series of the space–time-filtered OLR during the same period. Day 0 refers to the peak convection day, and positive (negative) lags indicate the time after (before) the peak convection day.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

The patterns of evolution of the diabatic heating rate (Q1; Yanai et al. 1973; Yanai and Tomita 1998) reveal the typical top-heavy heating profiles on “day 0” for all three analyses (Fig. 4), which is consistent with many previous studies (e.g., Lin et al. 2004; Morita et al. 2006). But similar to the RH composites, the MJO signals in Q1 in the NOGAPS analysis are weaker (by about 10%) than those in the ERAI or GFS analyses. The shallow heating mode prior to the peak convection is hardly discernible in the NOGAPS analysis. This may be either due to a stronger stratiform process and/or the lack of cumulus congestus [shallow and congestus convection; e.g., Schumacher et al. (2004); Khouider and Majda (2006); Chikira (2014)]. The relatively weak MJO signals in the NOGAPS thermodynamic fields may be attributed to the deficiencies in the model physics, particularly the Emanuel cumulus scheme. Compared to some other cumulus schemes, the Emanuel scheme is largely insensitive to the tropospheric moisture and is not well suited to represent cumulus congestus due to untreated subgrid perturbations that tend to enhance vertical development. The insensitivity of convection to free-tropospheric moisture may contribute to weak MJO-like coherences (Grabowski and Moncrieff 2004).

Fig. 4.
Fig. 4.

As in Fig. 3, but for the diabatic heating rate (Q1). Unit is K day−1.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

5. Evaluation of the NOGAPS short-term forecasts

As the second part of this study, the tropical mean state, thermodynamic fields, and the forecast skill of the MJO in the NOGAPS short-term forecasts are evaluated against the ERAI and satellite data. Previous studies have found that the bias of intraseasonal variations is closely related to the mean state bias (e.g., Kim et al. 2011; Yang et al. 2012). Additionally, the tropical–extratropical teleconnections are also sensitive to the mean flow structure (Wang et al. 2005). We will thus first examine the seasonal mean patterns.

a. Precipitation spatial pattern and ITCZ bias

Skillful precipitation forecasts remain a challenge to the modeling community. Since the observed precipitation data are not directly ingested into the model through data assimilation, precipitation evaluation can reveal the deficiencies in model physics (Trenberth et al. 2003; Dai 2006; Janowiak et al. 2010). Figure 5 shows the average daily precipitation rate in the tropics (15°S–30°N) during 2008–11 (for the time period when the NOGAPS forecasts are available) derived from CMORPH, the NOGAPS 6-day forecasts, the GFS 6-day forecasts, and the biases of the NOGAPS and GFS forecasts with respect to CMORPH. Comparisons with CMORPH show that both the NOGAPS and GFS forecasts capture the general patterns of tropical precipitation, including the narrow ITCZ band over the central and eastern Pacific and heavy precipitation over the Indo-Pacific warm pool. The NOGAPS 6-day precipitation forecast, however, has a prevailing dry bias over the tropics (Fig. 5c). In particular, precipitation is substantially underpredicted over the Indo-Pacific warm pool, the African monsoon and the eastern Atlantic ITCZ regions, and the central Pacific ITCZ region. Wet biases are found over the equatorial and southwest Indian Ocean and Central America. The GFS 6-day forecasts have overall wet biases over the tropics, but dry biases appear over the three ascending centers of the global Walker circulation: the Indo-Pacific warm pool, equatorial South America (Amazon basin), and equatorial African regions; precipitation is also overpredicted in other regions, including the Maritime Continent and Southeast Asia. Similar spatial distributions of the precipitation biases are also found in the NOGAPS and GFS forecasts with different forecast lead times (not shown). Figures 5d and 5g show that the precipitation biases increase with the forecast lead time in most tropical regions in both models.

Fig. 5.
Fig. 5.

The average daily precipitation rate (mm h−1) in the tropics (15°S–30°N, 180°–180°) using all available data in boreal summer during 2008–11 for (a) CMORPH, (b) NOGAPS 6-day forecasts, (c) the difference between (b) and (a); (d) difference between the NOGAPS 6- and 2-day forecasts; and (e)–(g) as in (b)–(d), but for the GFS.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

A significant precipitation bias in the NOGAPS forecasts is the northward shift of the ITCZ. Figure 6 shows the precipitation averaged over the eastern Pacific (0°–25°N, 100°W–85°W) from the NOGAPS and GFS forecasts and CMORPH. Compared to CMORPH, the NOGAPS forecasts capture the magnitude of the ITCZ precipitation, but in the 2-day (4 and 6 day) forecasts the peak precipitation is displaced by about 1° (2°) northward. GFS captures the latitudinal location of the ITCZ but overpredicts the precipitation rate, and the wet bias increases with forecast lead time. Precipitation biases are associated with biases in latent heat release. The diabatic heating profiles and their impacts on the large-scale circulations will be examined in the next section. For brevity, we will focus on the NOGAPS forecasts.

Fig. 6.
Fig. 6.

The average hourly precipitation rate (mm h−1) over the eastern Pacific ITCZ region (0°–25°N, 100°–85°W) with different forecast lead times using all available data in boreal summer during 2008–11: (a) the NOGAPS forecast (colors) and CMORPH (black); and (b) GFS (colors) and CMORPH (black).

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

b. Diabatic heating and large-scale circulations

To evaluate the thermodynamic performance of the NOGAPS forecasts, Fig. 7 shows the vertical cross sections of the zonally averaged Q1 derived from the ERAI and the NOGAPS 3- and 5-day forecasts over the western Pacific (120°–150°E) (top), the eastern Pacific (115°–85°W) (middle), and the Indian Ocean (50°–80°E) (bottom). To examine the connections with the large-scale atmospheric circulations, Fig. 8 shows the vertical cross sections of the zonally averaged zonal or meridional winds of the ERAI (left column), the difference between the NOGAPS 3-day forecast and the ERAI (middle column), and the difference between the NOGAPS 5- and 3-day forecasts (right column). The different ocean basins are examined separately as follows.

Fig. 7.
Fig. 7.

Vertical cross sections of the zonally averaged diabatic heating rate (Q1; K day−1) using all available data in boreal summer during 2008–11 for (from left to right) ERAI and NOGAPS 3- and 5-day forecasts over the (top) western Pacific (120°E–180°), (middle) eastern Pacific (115°–85°W), and (bottom) Indian Ocean (50°–80°E).

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

Fig. 8.
Fig. 8.

Vertical cross sections of (top),(middle) the zonally averaged zonal (U) and (bottom) meridional (V) winds using all available data in boreal summer during 2008–11 for (left) ERAI, (middle) the NOGAPS 3-day forecast minus ERAI, and (right) the NOGAPS 5-day minus the NOGAPS 3-day forecasts over the (top) western Pacific (120°–150°E), (middle) eastern Pacific (115°–85°W), and (bottom) Indian Ocean (50°–70°E). Unit is m s−1.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

Over the western Pacific (Fig. 7, top), the NOGAPS forecasts capture the heating in the lower troposphere and the cooling above in 30°–10°S, which is associated with the marine stratocumulus. Over the western Pacific monsoon regions (10°S–20°N), however, where the NOGAPS forecasts have a dry bias (Fig. 5c), the diabatic heating is significantly underproduced, and the maximum heating occurs at an altitude slightly higher than that in the ERAI. The biases in precipitation and heating induce biases in the regional Hadley circulation. Figure 8 (top) shows that the tropical easterlies (trade winds) and subtropical westerlies in the Northern Hemisphere are both weaker in the NOGAPS forecasts compared to the ERAI, while the tropical easterlies south of 10°S are stronger. The analysis suggests a weaker northern Hadley cell over the western Pacific.

In the eastern Pacific ITCZ region (Figs. 7 and 8, middle), the lower-tropospheric heating shifts northward and upward with forecast lead time, which is associated with the northward shift of precipitation (or the ITCZ) (Figs. 5c and 6a). The heating profile in the NOGAPS 5-day forecast is more top heavy compared to the ERAI, which implies a strong stratiform component. Figure 8 shows that the westerlies to the south of the ITCZ shift northward with the heating. Due to the resultant changes in the steering flow and vertical shear, the biases in precipitation and diabatic heating may lead to biases in the tropical cyclone frequency, track, and intensity in the NOGAPS forecasts.

The NOGAPS forecasts overpredict precipitation over the western south Indian Ocean and underpredict precipitation over the eastern Arabian Sea. Figure 7 (bottom) shows that both the diabatic cooling north of 10°N and heating south of 10°N are significantly overpredicted. Similar to the eastern Pacific, the heating over the South Indian Ocean is also more top heavy than that in the ERAI. Figure 8 (bottom) reveals weaker cross-equatorial flow (including the Somali jet) over the Indian Ocean in the NOGAPS 5-day forecast (southerlies weakened by more than 30%). This is due to the antisymmetric heating distribution straddling the equator over the Indian Ocean. Weaker cross-equatorial flow implies reduced moisture transport, which may affect the MJO initiation and the monsoon onset over the North Indian Ocean. All the biases in the large-scale atmospheric circulations amplify with the forecast lead time and are closely related to the biases in the diabatic heating and precipitation fields (Figs. 7 and 8, right).

c. Moist processes in the NOGAPS

The strong control of CWV on precipitation has been examined in some previous studies (Zeng 1999; Bretherton et al. 2004; Neelin et al. 2009; Alaka and Maloney 2012). The relationship between humidity and convection is also a central issue of many cumulus schemes. The sensitivity of the MJO simulations to the model cumulus scheme has been reported previously (e.g., Chao and Lin 1994; Wang and Schlesinger 1999; Maloney and Hartmann 2001). Figure 9 shows the average daily precipitation rate stratified with 1-mm-wide bins of the CWV over four tropical ocean basins (20°S–20°N). SSM/IS retrievals of the precipitation rate and CWV plotted in Fig. 9 provide an ideal standard for examining the relationship between precipitation rate and the CWV. In addition to the SSM/IS, CMORPH precipitation rates are plotted against the ERAI CWV, and the precipitation rates and CWV from the NOGAPS 1-, 3-, and 5-day forecasts are also shown. All the datasets capture the nonlinear relationship between precipitation rate and the CWV: a low precipitation rate in the presence of less CWV followed by an exponential increase in the precipitation rate with increasing CWV. A close look at the data, however, reveals some quantitative differences. Plots of the CMORPH precipitation versus SSM/IS CWV are quite close to the SSM/IS precipitation rate versus SSM/IS CWV curves for all the basins (not shown), which is expected because the CMORPH results are derived exclusively from passive microwave retrievals. The CMORPH versus ERAI CWV curves, however, are above the SSM/IS precipitation rate versus SSM/IS CWV curves for all the basins, indicating that ERAI underestimates the CWV for a given precipitation rate. For example, the precipitation rate of 15 mm day−1 is associated with 64-mm CWV in the SSM/IS data but with 57-mm CWV in the ERAI data over the Indian Ocean. Compared to other basins, the difference between the CMORPH versus ERAI CWV curve and the SSM/IS curve is relatively small over the Indian Ocean. The precipitation rate versus the CWV curves derived from the NOGAPS forecasts are between the CMORPH versus ERAI CWV curves and the SSM/IS curves for all the basins, and the 3- and 5-day forecasts are closer to the CMORPH versus ERAI CWV curves than to the SSM/IS curves. Similar to ERAI, NOGAPS also underestimates the CWV for a given precipitation rate. Or, in other words, the NOGAPS forecasts and ERAI generate too much precipitation for the same amount of CWV. This finding indicates that the heavy precipitation may initiate too early in the models in terms of the CWV threshold.

Fig. 9.
Fig. 9.

The average daily precipitation rate (mm day−1) stratified with 1-mm-wide bins of the CWV (mm) using all available data over four tropical ocean basins (20°S–20°N) in boreal summer during 2008–11. Shown are the curves for the (a) Indian Ocean (30°–120°E), (b) western Pacific (120°E–180°), (c) eastern Pacific (180°–80°W), and (d) Atlantic (80°W–30°E). The black solid lines indicate the SSM/IS precipitation rate vs the SSM/IS CWV; black dotted lines show the CMORPH precipitation rate vs the ERAI CWV; red, green, and blue lines show the 1-, 3-, and 5-day precipitation rates, respectively, of the NOGAPS forecast vs the 1-, 3-, and 5-day CWVs of the NOGAPS forecast.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

The probability distribution functions (PDFs) of the CMORPH precipitation rate with 15-mm-wide precipitation bins (log10%) are shown in Fig. 10a. The frequency of occurrence decreases with the precipitation rate. Figure 10b shows the percent deviation of the NOGAPS 3-day forecasts from CMORPH. NOGAPS underpredicts light and moderate-to-heavy (13–182 mm day−1) precipitation but overpredicts extremely heavy rainfall (greater than 190 mm day−1). The frequency of the drizzle-like precipitation is captured quite well by the NOGAPS forecasts. Figure 10c shows the standardized PDF difference of the precipitation rates between the NOGAPS 5- and 3-day forecasts. As the forecast lead time increases, weak to moderate-to-heavy rainfall (15–150 mm day−1) occurs more frequently, but the occurrence of extremely heavy rainfall (>200 mm day−1) is reduced by 5%. The reduction of the heavy precipitation is consistent with the amplifying dry bias in the CWV with the forecast lead time, as shown next in Fig. 11. The increases in the other types of precipitation with forecast lead time imply that the NOGAPS 5-day forecast exhibits a greater degree of recovery from a biased initial state (Fig. 3) toward a realistic state than does the 3-day forecast.

Fig. 10.
Fig. 10.

(a) PDFs (log10%) of the average CMORPH precipitation rate over the tropical ocean basins (20°S–20°N) stratified into 15-mm-wide bins. (b) The relative PDF differences of precipitation rate (%) over the tropical ocean basins between the NOGAPS 3-day forecast and CMORPH. (c) As in (b), but for differences between the NOGAPS 5- and 3-day forecasts. The rainfall types are defined according to the classifications in the Glossary of Meteorology: drizzle (0–12 mm day−1), light (13–60 mm day−1), moderate (61–182 mm day−1), and heavy (>182 mm day−1).

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

Fig. 11.
Fig. 11.

As in Fig. 9, but for the CWV probability distribution (%). The black solid (dotted) lines show the SSM/IS (ERAI) CWV; the faint yellow lines are for the NOGAPS analysis; and the red, green, and blue lines show the 1-, 3-, and 5-day NOGAPS forecasts, respectively.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

The probability distribution of the CWV over each basin is shown in Fig. 11. The SSM/IS reveals the different column moisture distributions over different basins. The western Pacific is characterized by a prominent peak between 55 and 60 mm, and low CWV values occur relatively less frequently compared to the other basins. The eastern Pacific has a bimodal distribution, with the primary peak of frequency of occurrence around 30 mm and a secondary one around 57 mm, which reflects dry conditions over the southeastern Pacific (with prevailing marine stratus) and moist conditions in the ITCZ region. Consistent with Fig. 9, Fig. 11 shows dry biases in the NOGAPS forecasts. The CWV of the peak frequency of occurrence in the NOGAPS forecasts is more than 5 mm lower than that in the SSM/IS over the Indian Ocean and the western Pacific. Over the eastern Pacific and the Atlantic, the NOGAPS forecasts do not capture the bimodal distribution of the CWV. The CWV of the primary peaking frequency over the Atlantic is up to 10 mm lower than that in the SSM/IS. A weaker dry bias is also present in the distribution of the CWV from the NOGAPS analysis, which is consistent with our diagnosis of the NOGAPS analysis in section 4. A close look shows a small but discernible increase in the dry bias over all the basins from 1- to 5-day forecasts, indicating deficiencies in the model physics.

A similar dry bias in the CWV distribution is also found in ERAI, but overall ERAI is more realistic than the NOGAPS forecasts. For example, the ERAI has a smaller dry bias over the Indian Ocean and the western Pacific, and it captures the bimodal distribution over the eastern Pacific. The upgrade of the vapor absorption model in the version-7 SSM/IS data partly explains the dry biases in the ERAI and the NOGAPS analyses, because both data assimilation systems ingested an earlier version of SSM/I–SSM/IS. Compared to the earlier versions, the CWV in the version-7 SSM/IS results slightly shifts to higher vapor values (above 55 mm) globally and is in better agreement with the GPS-derived vapor (K. Hilburn 2013, personal communication).

To further investigate the moisture biases in the NOGAPS forecasts, we examined the vertical profiles of RH for different CWV thresholds (Fig. 12). Although SSM/IS provides a realistic look at the relationship between precipitation rate and CWV, it does not provide information on the vertical distribution of water vapor. The moisture data in the Atmospheric Infrared Sounder (AIRS) suffer from cloud/rain contamination (Fetzer 2006), and are found to have apparent problems in regions with high precipitation rates (not shown). Despite the dry bias in the CWV and its imperfect relationship with precipitation, ERAI likely provides the best available 3D moisture field, and has a more realistic moisture profile than does NOGAPS.

Fig. 12.
Fig. 12.

The RH (%) stratified with 1-mm-wide bins of the CWV (mm) using data in boreal summer during 2008–11 over all the tropical ocean basins (20°S–20°N, 180°–180°), from (a) ERAI; (b) the NOGAPS 3-day forecast; (c) the relative percentage difference between the NOGAPS 3-day forecast and ERAI; and (d) as in (c), but for the difference between the NOGAPS 5- and 3-day forecasts.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

Figure 12a illustrates the averaged ERAI RH stratified with 1-mm-wide bins of the CWV over all the tropical ocean basins (20°S–20°N, 180°–180°) It shows that RH exceeds 70% below 900 hPa for all the CWV results between 20 and 65 mm. For the smaller CWV, RH decreases sharply above 900 hPa, and a significant dry layer with RH less than 20% is present in the middle troposphere for the CWV less than 30 mm. For high CWV values, large RH extends to the upper troposphere, likely due to the convective activities associated with high CWV. While a moist column is favorable for convection, convection transports moisture from the planetary boundary layer and further moistens the free troposphere.

Figures 12b and 12c show that the NOGAPS forecasts broadly resemble the ERAI simulations with some quantitative differences. In particular, the NOGAPS forecasts have a dry bias (up to 6%) within the marine boundary layer (MBL) and a moist bias above 800 hPa compared to ERAI. The dry bias in the lower troposphere is particularly large for CWV greater than 33 mm. Since the MBL contains most of the column moisture, the dry bias in the MBL is consistent with the dry bias in the CWV. Additional diagnostics of the moisture tendency terms and cloud fractions in short-term hindcasts show that deep convection increases with forecast lead time for CWV > 50 mm. It is possible that the dry bias in the MBL is due to a hyperactive convection in the NOGAPS model, in which convection is triggered too early in terms of the CWV threshold and moisture does not accumulate as much as it should within the MBL. Hyperactive deep convection may also result in an underrepresentation of shallow convection. Another possibility is the lack of mixing by large eddies in the convective boundary layer. On the other hand, readers should be cautioned about possible biases in the ERAI moisture field. Kishore et al. (2011) compared three reanalysis datasets (ERAI, NCEP–NCAR, and JRA-25) with the six-satellite-based Constellation Observation System for Meteorology Ionosphere and Climate (COSMIC) GPS-based radio occultation (GPS-RO) retrieval. They showed that ERAI is closest to the COSMIC measurements among the three reanalysis datasets but has a dry bias in the free atmosphere and a moist bias at 1.4 km. This suggests that the moisture biases in the NOGAPS forecasts may not be as strong as indicated in Fig. 12c. In addition, Pierce et al. (2006) found that it is a common issue for climate models to have a dry bias below 800 hPa and a moist bias in the middle to upper troposphere (between 300 and 600 hPa). The difference between the NOGAPS 3- and 5-day forecasts suggests that the moisture difference between the ERAI and the NOGAPS forecasts is amplified with forecast lead time (Fig. 12d).

It is instructive to examine the vertical profiles of Q1 in the ERAI and the NOGAPS forecasts. The heating profile of ERAI (Fig. 13a) shows a bimodal structure for CWV less than 50 mm, with one maximum around 850 hPa and another around 500 hPa, which correspond to shallow and deep convection, respectively. For CWV > 50 mm, Q1 has a top-heavy heating profile with a peak around 500 hPa. The NOGAPS forecasts have a similar top-heavy profile for high CWV (Fig. 13b), but underpredict Q1 below 400 hPa by up to 25% (Fig. 13c). The heating maximum around 850 hPa for low CWV is missing in the NOGAPS forecasts. The weak lower-tropospheric diabatic heating in the NOGAPS forecasts is either due to a stronger stratiform process (which is characterized by condensational heating above and evaporative cooling below the freezing level), or a deficiency of cumulus congestus due to untreated subgrid perturbations that tend to enhance vertical development, or both (e.g., Schumacher et al. 2004; Khouider and Majda 2006; Chikira 2014). Consistent with the moisture field, the negative biases in the lower-tropospheric diabatic heating increase with forecast lead time, whereas deep convective heating increases with forecast lead time for CWV > 50 mm (Fig. 13d).

Fig. 13.
Fig. 13.

As in Fig. 12, but for Q1. Unit is K day−1.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

d. MJO in the NOGAPS forecasts

The errors in the large-scale circulation patterns and the diabatic heating profile as observed in the NOGAPS forecasts may affect the short-term forecast skill of the MJO. Figure 14 shows the pattern correlations for the tropical (20°S–20°N, 180°–180°) 200-hPa zonal wind between the NOGAPS–GFS forecasts and ERAI in eight MJO phases based on the RMM. The predictability for the MJO drops quickly in phase 2 when the MJO is initiated over the Indian Ocean and in phase 5 when the MJO propagates across the Maritime Continent in both prediction systems, but it drops more quickly in the NOGAPS forecasts, especially during phase 2. The limited forecast skill of the MJO in phases 2 and 5 is a common issue in GCMs, either because of relatively low intrinsic predictabilities compared with other tropical regions or model deficiencies (Inness and Slingo 2003; Neale and Slingo 2003; Slingo et al. 2003). Given the reasonable representation of the MJO in the model initial conditions (i.e., the analysis data), the limited forecast skill implies the strength of NOGAPS in data assimilation and the weakness in the model physics. Also note that the spatial resolution of the NOGAPS model was coarser than that of the GFS during the period of diagnosis.

Fig. 14.
Fig. 14.

Spatial pattern correlations of tropical (20°S–20°N, 180°–180°) zonal winds at 200 hPa between forecasts from: (a) the NOGAPS and ERAI and (b) GFS and ERAI for eight MJO phases (WH04), using all available data in boreal summer during 2008–11. The red lines of decreasing thicknesses show the correlations for 1- to 6-day forecasts.

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

To investigate the possible deficiencies in the model physics that may influence the MJO forecast skill, the composites of RH and Q1 from the NOGAPS forecasts were constructed based on the local MJO index over the Indian Ocean. Five prominent MJO events in boreal summer during 2008–11 were selected (Fig. 15). Due to the gaps in the forecast data, the composite anomalies were constructed with respect to the daily mean derived from the 2004–10 NOGAPS analysis (the same as in Figs. 3 and 4). The composite anomalies thus include both the MJO signals in the NOGAPS forecasts and the difference between the NOGAPS analysis and forecasts. The composite of RH derived from the 3-day NOGAPS forecast is shown in Fig. 15a, and is characterized by positive anomalies above 800 hPa and negative anomalies below. The maximum moistening on day 0 at 400 hPa is consistent with Fig. 3, but has a larger magnitude. Strong positive RH anomalies are also found around day −15 at 400 hPa, which may be due to the relatively small sample size. The negative anomalies below 800 hPa are consistent with the dry bias identified in Fig. 12a. Furthermore, the nearly constant magnitude of the negative RH anomalies at different MJO phases suggests that the RH anomalies in Fig. 15a are primarily dominated by the differences between the NOGAPS analysis and forecasts, and that the premoistening and postdrying in the lower troposphere are largely missing in the NOGAPS forecasts. The RH profiles on day 0 from the ERAI, the NOGAPS analysis, and the forecasts are shown in Fig. 15c, which further illustrate the positive (negative) biases in the middle and upper (lower) troposphere in the NOGAPS forecasts. The composites of Q1 are shown in Figs. 15b and 15d. Although the Q1 profiles in the NOGAPS forecasts are close to that derived from ERAI or the NOGAPS analysis (Fig. 15d), the temporal evolution of Q1 (Fig. 15b) shows no indication of transition from shallow convection to deep convection and then to stratiform precipitation. Again, it shows very weak MJO signals in the diabatic heating fields of the NOGAPS forecasts. The lack of cloud transitions can probably be attributed to the insensitivity of the Emanuel scheme to the tropospheric moisture (the moisture dependence in the scheme is only through the virtual temperature effect on the parcel buoyancy) and is not well suited to represent cumulus congestus due to untreated subgrid perturbations that tend to enhance vertical development. The weak MJO signals in the NOGAPS forecasts thus imply the deficiency in the RH-dependent formulation for organized deep entrainment in the model, which has been found to be crucial to an improved simulation of the MJO (e.g., Chikira and Sugiyama 2013; Chikira 2014; Hirons et al. 2013a; Hirons et al. 2013b).

Fig. 15.
Fig. 15.

(a) Composite anomalies of RH (%) for five MJO events during 2008–11 derived from the NOGAPS 3-day forecasts with respect to the daily average of the NOGAPS analysis during 2004–10. (b) As in (a), but for Q1 (K day−1). (c) The RH profiles (%) on day 0 from ERAI (black), the NOGAPS analysis (faint yellow), and the 3-day (green) and 5-day (blue) NOGAPS forecast. (d) As in (c), but for Q1 (K day−1).

Citation: Weather and Forecasting 29, 4; 10.1175/WAF-D-14-00010.1

6. Summary and discussion

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational short-term forecasts are evaluated systematically against the ERA-Interim Re-Analysis (ERA-Interim; ERAI) and Global Forecast System (GFS) analysis and forecasts, with a special focus on the following aspects: (i) the mean states, (ii) the Madden–Julian oscillation (MJO), and (iii) the precipitation and moist processes. A suite of diagnostic tools has been developed for the model evaluation using both performance- and physics-based metrics. It is found that the MJO signals in the dynamic fields of the NOGAPS and GFS analyses are in good agreement with those in the ERAI. The NOGAPS analysis captures the lower-tropospheric moistening and warming leading to the peak convection of the MJO and the lower-tropospheric drying and cooling following the peak, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in ERAI or in the GFS analysis.

The precipitation distributions in the NOGAPS and GFS short-term forecasts are in reasonable agreement with CMORPH. The NOGAPS forecasts have a dry bias over the Indo-Pacific warm pool, the African monsoon region, the eastern Atlantic ITCZ region, and the central Pacific ITCZ region. In particular, the ITCZ shifts northward with forecast lead time in the NOGAPS over the eastern Pacific where the GFS overpredicts the ITCZ precipitation. In regions with large precipitation biases, the diabatic heating profile is found to be more top heavy in the NOGAPS forecasts than in ERAI. The errors in the diabatic heating field are associated with errors in large-scale circulations, including weaker trade winds and weaker Hadley and Walker circulations over the western Pacific and weaker cross-equatorial flow over the Indian Ocean.

The relationship between the precipitation and column water vapor (CWV) in the NOGAPS forecasts is evaluated against SSM/IS, CMORPH, and ERAI. It was found that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV threshold. On the other hand, the NOGAPS forecasts have a dry bias in terms of the probability distribution of the CWV. The vertical profile of RH further shows that the NOGAPS forecasts have a dry bias within the boundary layer and a moist bias in the middle and upper troposphere. The dry biases in the ERAI and NOGAPS analyses can be partly attributed to the old version of SSM/I and/or SSM/IS used in the data assimilation systems. However, a subtle but consistently amplified dry bias with the forecast lead time suggests deficiencies in the model physics. NOGAPS also underpredicts the light to heavy precipitation (13–182 mm day−1), overpredicts extremely heavy rainfall, and reproduces the drizzle-like precipitation quite well. The diagnostic of Q1 shows that the shallow heating mode is missing for CWV < 50 mm in the NOGAPS forecasts.

The prediction skills of the MJO in the NOGAPS and GFS operational forecasts are evaluated using pattern correlations. It is found that the MJO prediction skill drops quickly with the forecast lead time in both models, especially for phases 2 and 5, and it drops more quickly in NOGAPS for phase 2. The weakest forecast skill levels in phases 2 and 5 of the MJO are consistent with previous findings about the limited predictability of the MJO at its initiation over the Indian Ocean and when its active convection center is near the Maritime Continent. The composite of the RH based on the self-defined local MJO index over the Indian Ocean reveals a dry bias within the boundary layer in the NOGAPS forecasts. The MJO signals are shown to be very weak in both the RH and Q1 fields, similar to the NOGAPS analysis, and cloud transition is also missing in these thermodynamic fields.

The moisture biases in the NOGAPS forecasts are possibly due to hyperactive deep convection, a deficiency of cumulus congestus, or/and lack of mixing by large eddies in the convective boundary layer. New cumulus and boundary layer parameterization schemes have been developed and implemented in the NAVGEM model, which is expected to better represent moist processes. It is hoped that the diagnostics presented in this study may shed light on future model improvements. The evaluation of the NAVGEM model is under way and will be reported upon in due course.

Acknowledgments

The authors thank Dr. Maria Flatau and Dr. Tim Dunkerton for helpful discussions and three anonymous reviewers for their helpful and constructive comments on the submitted manuscript of this article. We also thank Dr. Michael Montgomery for providing the NOGAPS and GFS forecast data and NOAA/OAR/ESRL/PSD for providing the ERA-Interim data. This work is supported by Office of Naval Research Grant N00014-11-1-0446.

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